物聯網資安產業輔導交流會 2023年4月27日(星期四) 10:00~12:30 台北南港展覽 1館 4樓 1202會議室 本會與 A&S安全與自動化雜誌 於2023年「Secutech台北國際安全科技應用博覽會」 4/27(四) 共同舉辦「物聯網資安產業輔導交流會」， 邀請大家共同分享此趨勢性的重要議題～ 台北南港展覽 1館 4樓 1202會議室 安防公會、A&S安全與自動化雜誌 交流會報名連結（已結束） https://www.asmag.com.tw/seminar/2023_iot_security/register.aspx 報名洽詢 A&S安全與自動化雜誌 02-8729-1099 分機215 鄭先生 jason.cheng@taiwan.messefrankfurt.com 1/1 < Previous Project Next Project >

< Back SECPAAS 資安服務模式與企業資安評級介紹 線上視訊會議 2021 0729 14:00~16:00 由經濟部工業局主辦、工業技術研究院執行、本會承辦的企業資安評級工具推廣活動，分為二階段進行，首發，於7月29日由工研院資通所資通系統與資料安全組副組長卓傳育博士為大家介紹資安整合服務平台 Security Platform as a Service (SECPAAS)，而資策會資安科技研究所高傳凱博士則是分享了國際間多則資安攻擊的慘痛案例，本會也利用此次機會安排台北市電腦公會行動應用聯盟對物聯網設備資安認驗證推動進行說明，三位講師生動、精闢的演說獲得線上與會43位來賓熱烈的回響。 < Previous News Next News >

CyberArk：生成式 AI 在 2024 年將引發災難性網路攻擊事件 2023-11-23 TechNews 科技新報 新聞來源： https://infosecu.technews.tw/2023/11/23/cyberark-generative-ai-security/ CyberArk 2023 身分安全威脅情勢報告，亞太地區 61% 的企業組織表示他們無法阻止或甚至偵測源自供應鏈的攻擊。 預期供應鏈攻擊，特別是供應鏈連鎖攻擊，將在 2024 年增加。 身分安全廠商 CyberArk 稍早公布了一份針對 2024 年資安趨勢的預測，包括 AI 和生成式 AI 在網路犯罪中的應用、雲端 Tier-0 資產作為高價值目標、供應鏈連鎖攻擊、連線劫持和 Cookie 竊取、安全瀏覽和 Web 隔離、PassKeys 無密碼身份驗證、SaaS 的相關法規鬆綁等方面。新興的攻擊手段和策略值得關注，防禦技術創新也讓企業組織有機會部署更堅固的資安環境。 網路犯罪應用 AI 和生成式 AI 在網路犯罪中的應用將持續加速。據 PwC 2024 年全球數位信任洞察調查，多數受訪者（52%）表示，生成式 AI 將在 2024 年引發「災難性」的網路攻擊。其中一個關鍵趨勢是更多人使用即時深度偽造（deep fake）技術讓社交工程攻擊更強悍，並逐漸成為網路攻擊的主流手段。攻擊者正在利用先進的 AI 能力來創建逼真的假身分，來欺騙個人或系統，以達成其犯罪目的。 雲端 Tier-0 資產為高價值標的 隨著越來越多的企業組織遷移到雲端基礎設施，Azure Active Directory（AzureAD）、AWS Identity and Access Management（IAM） 和身分即服務（IDaaS） 等 Tier-0 資產正逐漸成為新的「王國之鑰」（意為掌握權力的鑰匙）。 因此它們正在成為網路攻擊誘人的標的。 CyberArk 預計，破壞這些雲端 Tier-0 資產的攻擊將會激增。 攻擊者也可能會利用供應鏈連鎖攻擊來針對這些標的進行攻擊。 供應鏈連鎖攻擊 CyberArk 2023 身分安全威脅情勢報告，亞太地區 61% 的企業組織表示他們無法阻止或甚至偵測源自供應鏈的攻擊。 預期供應鏈攻擊，特別是供應鏈連鎖攻擊，將在 2024 年增加。供應鏈連鎖攻擊是供應鏈攻擊的一種類別，駭客首先獲取對一個系統的存取權，然後使用該存取權來侵入與之相連的其他系統。 由於可有效地迴避堅固的防守目標，此類攻擊正不斷在增加。攻擊者會利用互連、受信任但較脆弱的目標中的漏洞，以滲透安全等級更高的系統。 Session Hijacking and Cookie Theft 連線劫持和 Cookie 竊取 連線劫持和 cookie 盜竊在網路攻擊中變得越來越普遍， 利用這些技術竊取使用者連線資訊和 cookie，即可繞過身分認證存取 Web 服務和帳戶內容。 隨著對線上服務和應用程式的日益依賴，這類攻擊將會更常見。 Secure Browser 和 Web 隔離 由於需要減輕與連線劫持、cookie 盜竊和其他基於 Web 的威脅相關的風險，安全瀏覽器和 Web 隔離技術被普遍採用。 許多企業認識到 Web 瀏覽器的漏洞日益嚴重，因此正在研究評估或實施Web 內容隔離的技術以強化安全性。 使用 PassKeys 進行無密碼身分驗證 無密碼身分驗證正在蓬勃發展，特別是隨著 Google、蘋果和微軟等公司將 passkey 密碼金鑰整合到其系統中， 企業組織已開始規劃或進行使用這種更安全身分驗證方法的無密碼專案。 無密碼身份驗證排除了傳統密碼可能存在的弱點。 受監管行業採用 SaaS 的相關法規鬆綁 由於缺乏立法支持或法規模糊，金融服務（FSI）和關鍵資訊基礎設施（CII）等受監管行業歷來對採用軟體即服務（SaaS）資安產品猶豫不決。 然而，許多國家的監管機構正在修訂、放寬或釐清採用 SaaS 資安服務的相關準則。 這樣的法規變革將使更多受監管的行業能夠採納和部署基於AI和大數據等先進技術的解決方案，以強化其資安體質。 < Previous News Next News >

2023台灣創新技術博覽會 遇見「數位資安」的創新moda 2023-09-29 數位發展部數位產業署 新聞來源： https://moda.gov.tw/ADI/news/latest-news/8363 12項資安及5G專網相關創新技術成果 賦能台灣打造具韌性的數位國家 AI、5G、大數據等前瞻科技加速智慧生活的到來，於此之際，國家產業在發展智慧應用時須同時掌握資訊安全議題。為讓產業與民眾找到科技創新與隱私保護的平衡點，數位發展部過去一年多來致力於推動相關計畫，協助產業在發展創新也能顧及資訊安全，精彩亮點成果都將在10月12日登場的「2023台灣創新技術博覽會Taiwan Innotech Expo」中，在台北世貿一館的創新領航館「數位資安」專區之中，精彩揭露。 數位發展部指出，「數位資安」專區集結資策會、工研院、電信中心三大法人機構，與2家國際廠商的創新技術，以「數位驅動．創新moda」為主題，分成「資安防護」與「通傳科技」兩個項目類別，一次展示12項與「資安」及「5G專網」相關的創新技術亮點成果。 在「資安防護」項目中，包括「一站式5G專網資安管理維護系統」、「隔空抓鑰–晶片旁通道攻擊檢測技術」、「SECPAAS零信任資安解決方案」、「工控場域AI偵防分析技術」、「APT攻擊狙殺鏈獵捕平台」、「SEMI E187設備資安標準規範」、「符合服務水準協議之智慧工廠」、「具隱私強化技術之遠距復健數據交換機制」8項技術；「通傳科技」項目包括「個人化全時全方位智慧監測輔助系統」、「心腹超音波多角度貼合擬真操作」、「5G元宇宙專業職能作業訓練系統-CNC銑床操作訓練」、「5G專網十大產業應用-以肉品加工產業為例」4項技術。精采的技術創新內容將讓與會者，一次盡覽數位發展部推廣科技創新與資訊安全技術的豐碩成果。 精彩的展出之外，數位發展部也在展出期間（10月13日）精心舉辦「5G資安協作共創論壇」，活動現場將先由帕羅奧圖網路台灣區總經理尤惠生、台灣野村總研諮詢顧問公司副總經理陳志仁兩位專家，分別以「全球資安創新與市場趨勢」與「全球5G專網與資安發展觀測」為主題進行深度演講，接續還將一次揭露辰隆、亞旭、和碩3家公司，闡述其導入5G專網的場域驗證過程與經驗。 相信，具廣度的展出將讓與會者掌握全球最新資訊安全技術發展與應用趨勢，而專業的論壇活動，更可以促成產業的深度交流。讓未來產業在推動數位轉型時，都能有資安技術隨行，進而形成台灣更開放、包容的創新環境，加速台灣打造具韌性的數位國家。 【轉知】活動訊息：10/12~10/14「 2023 年台灣創新技術博覽會」 https://www.tiaiss.org.tw/eventinfo/10%2F12~10%2F14%E3%80%8C-2023-%E5%B9%B4%E5%8F%B0%E7%81%A3%E5%89%B5%E6%96%B0%E6%8A%80%E8%A1%93%E5%8D%9A%E8%A6%BD%E6%9C%83%E3%80%8D 「 2023 年台灣創新技術博覽會」官網：https://tie.twtm.com.tw/zh-tw 數位發展部首次登場「台灣創新技術博覽會」推動產業韌性 引領數位創新 https://moda.gov.tw/ADI/news/latest-news/8485 < Previous News Next News >

國外科技業者CIO分享5個資安準備與觀點 2024-01-18 說資安新聞網 新聞來源： https://cybersecurenews.com.tw/news-edit-079/2/ Omer Grossman是CyberArk的全球首席資訊長，關於2023年3件資安威脅情勢，促使他對於2024年有5個身為資安長保護企業組織的觀點分享。 這是第三篇2024資安的預測，前面兩篇分別透過資安廠商的情資威脅中心分析或是服務客戶經驗所進行的觀察，進而分享2024的資安預測，第一篇來自 Fortinet 公布《2024全球資安威脅預測》 ; 第二篇是 Seagate 分享2024科技與資料儲存趨勢的觀察 ，第三篇的內容與其說是資安預測，更可形容為是擔任科技產業一名資訊長CIO（Chief Information Officer，CIO）其歷經2023年的資安情勢後，對今年資安準備與觀點的分享。 Omer Grossman是身分安全全球資安服務廠商CyberArk的全球首席資訊長。他於一月在部落格裡寫了一封信，關於2023年3件資安威脅情勢，促使他對於2024年有5個身為資安長保護企業組織的觀點分享。 Omer Grossman表示影響他對於2024年身為CIO的思考，來自於去年的3件資安威脅情勢，分別是軟體供應鏈(Software Supply Chain)、GenAI的技術成熟度曲線與採用率(GenAI’s Hype Cycle and Adoption)，以及全球政治經濟的不確定性(Global Political and Economic Uncertainty)。 軟體供應鏈 企業間供應鏈的關係，之所以可以如此協調順暢，來自於彼此間的信任，因此信任成為關鍵的因素，同樣的也成為駭客組織利用的手段，破壞企業對外合作夥伴的信任，包含第三方軟體( Third-party software )的使用、供應商關係等。例如2020年底，網路監控產品SolarWinds Orion系統的資安威脅事件，Grossman文中也舉例去年年初VoIP IPBX軟體開發商3CX為例，3CX程式被駭客植入藏了木馬病毒，連帶讓其Window及macOS的用戶受到病毒的感染。 GenAI的技術成熟度曲線與採用率 對於身為技術專家Grossman，近幾年生成式人工智慧（Generative AI，GenAI）的快速竄起、創新的技術源源不絕的現象，他也表示其心情既興奮也不安，不安的是擔心被有心人士將GenAI當作犯罪的工具，所幸全球似乎也正視其可能會有的憂心，美國於去年10月簽署一份關於推動與治理人工智慧(AI)的行政命令，以及去年12月初，歐盟推出人工智慧法案(AI Act)達成政治協議，這也是全球第一個AI綜合法令框架，用於規範企業組織、系統供應商等。 全球政治經濟的不確定性 Grossman在文中也提到，去年世界各地因為受到地緣政治衝突的影響，許多重大關鍵基礎設施和供應商的生態系統均遭受攻擊破壞，至今尚未停歇，對於即將舉行全國選舉的美國、印度和英國，她們均是世界五大經濟體之一的國家，對於可能會有的國家民族行為或是網路犯罪分子間，所產生的攻擊意圖或是破壞活動都是令人擔憂與要有所警覺的。 Grossman身為資安長保護自身企業安全，提供2024年5個觀點 定期評估供應商的安全能力 定期評估供應商，宛如企業組織定期的進行企業資訊安全檢測，這個動作不只是讓企業組織當下可以安心運作，同時也是幫企業組織買保險，以防萬一。 切記，人工智慧系統的定義不僅限於 GenAI Grossman提醒我們，人工智慧系統包括 GenAI 以及使用神經網路的系統和長期部署建構的設備，因此2024 系統的評估、保護和管理等至關重要。他建議企業要考慮簽訂合約協議時，在協議中能允許企業組織基於風險的治理和風險管理政策，可以定期審查供應商針對從任何身分，使用人工智慧的產品和系統。 回到基礎面並做好它(Go back to the basics and do them right.) 此建議的重點是改善與回到基礎面，員工培訓、組織安全的基本觀念、長期定時做系統的威脅檢測與回應、提高網路安全技能等，無論科技如此再進步與新穎，基礎功夫是王道。 實施強而有力的零信任策略 零信任之旅，現在您應該是在路上，請不要再等待，猶豫不決了。 將員工視為最大的資產，而不是最薄弱的環節(Treat your people as the greatest asset, not the weakest link.) Grossman說，我們可以擁有世界上所有的工具來保護我們的組織，但最有價值的資產是人——網路安全專家、員工、承包商和合作夥伴等等。一定要珍惜、培育他們。歸根究底，一切都與人有關。 Omer Grossman於部落格最後還說的一段耐人尋味的話，他說:「作為資訊長和技術領導者，我建議您先退後一步，找到平衡，在解決新問題時，請不要著急。」 這一篇則是第三篇2024資安的預測，希望第三篇從一名科技產業資訊長(CIO)觀點與建議的觀點角度，提供您具體方向，保護您的企業組織、公司員工與合作夥伴。 相關閱讀: [2024資安預測 3-1] Fortinet 公布《2024全球資安威脅預測》 [2024資安預測 3-2] Seagate 分享2024科技與資料儲存趨勢的觀察 < Previous News Next News >

麥肯錫報告 ①｜疫情發展的兩種可能！最糟要等這時候才復甦 2020-05-19 《經理人》 新聞來源： https://www.managertoday.com.tw/articles/view/59433 新冠肺炎（COVID-19，俗稱武漢肺炎）蔓延全球，儘管東亞地區看似趨緩，但歐美多國像是義大利，卻陷入指數型爆發。除了個人關心如何防疫、自保之外，企業怎麼在這波疫情中存活，也是迫切的議題。 新冠肺炎（COVID-19，俗稱武漢肺炎）蔓延全球，儘管東亞地區看似趨緩，但歐美多國像是義大利，卻陷入指數型爆發。除了個人關心如何防疫、自保之外，企業怎麼在這波疫情中存活，也是迫切的議題。 麥肯錫（McKinsey & Company）公司以過去輔導企業的經驗、參考許多業界經驗，在官方網站發表一篇名為 COVID-19:Implications for business 的文章，並提供一份線上報告，提出未來景氣、企業應對方案的觀點。 疫情發展的兩種可能 麥肯錫原本以 3 種角度：快速復原（樂觀）、全球趨緩（當前情況）與全球大流行且經濟衰退（悲觀），預測了各個國家與市場的經濟走勢，不過到最近版本（3月16日）時，簡化為 2 種版本：延遲復原（delayed recovery）以及持續緊縮（prolonged contraction），應是剔除原本樂觀預期。 較樂觀情況：延遲復原，全球復甦可望落在第四季 新冠肺炎會受到季節影響，且檢測試劑數量將跟上需求。中國與東亞的疫情在第二季初期會受到控制，歐美地區確診人數則持續快速增加到 4 月中旬，各國將採取更強烈的防疫政策，疫情也會在 6 月趨緩。 經濟狀況受到大規模隔離、旅遊禁令影響，企業或消費者的支出都會大幅下降，即便是恢復速度比較快的亞洲地區，供應鏈直到第二季尾聲仍難以恢復，至少到第三季才有起色，歐美地區更可能等到第四季。各國政府則會更積極採取貨幣策略，像是美國聯準會已經連番降息，但麥肯錫認為此舉效果有限。 較悲觀情況：持續緊縮，直到明年前兩季才會復甦 挑戰更加嚴峻。病毒會被證實與季節無關，各國防疫策略效果也不彰，剛開始流行的歐美地區，確診高峰會延續到 5 月中旬，就連可能已經控制住疫情的東亞國家，也得持續實施防堵政策，避免疾病再度流行。 經濟情形不可避免更加糟糕，2020 年企業的裁員人數與破產數增加。 麥肯錫甚至認為全球經濟受損程度，接近 2008 年的金融危機，主要經濟體的 GDP 遭受打擊，直到 2021 年的前兩季才會復甦。 供應鏈受影響，衝擊比想像中大 這次新冠肺炎影響範圍擴及全部供應鏈（參見下圖），麥肯錫特別點名物流系統，是任何產業都會遇到的挑戰，加上比起 2003 年 SARS 疫情， 全球更依賴中國供應鏈，影響會比想像中大。 另外，即使中國逐步復工，初期零件生產速度可能還跟不上需求，各企業依然得做好庫存管理，像是衡量二線供應商的可能性與風險，分散零組件來源，以及記得盤點物流商的能力，提早準備好預防措施。 < Previous News Next News >

台灣智慧安防工業同業公會 公會講座活動花絮 請點選相簿放大觀看 S__59613410 S__59613409 69764 S__59613410 1/12 DSC_3429 HS (112) 20201020_201020_6 DSC_3429 1/46 2020/12/18 邊緣 AI 運算於行動影像之應用 & 資安議題下的後海思時代、區塊鏈下談安防產業上鏈 講座 IMG_9185 IMG_9188 IMG_9181 IMG_9185 1/7 110.3.26 S__14557246 S__14557251 110.3.26 1/6 2021/03/26 AIoT安防新視界 講座 2021/06/23 智慧安防AI技術落地應用 線上研討交流會 S__59613410 S__59613409 20200807-第一次 S__59613410 1/14 2020 0814 人工智慧與深度學習於視訊監控之十八項武藝講座 HS (112) HS (111) 20201020_201020_6 HS (112) 1/46 2020 1016 公會辦公室啟用酒會 擷取 IMG_9188 IMG_9185 擷取 1/8 2020 1023 崛起於危機之中-談企業情境規劃講座 2021/07/22 線上第一屆第一次安控設備 資安技術暨介接標準委員會 2021/07/29 SECPAAS資安服務模式 與企業資安評級介紹 2021/08/11 門禁系統廠商意見徵詢 線上會議 2021/08/12 SECPAAS資安工具服務 線上體驗活動 2021/08/20 第一次門禁系統資安標準 制定草案小組 2021/09/03 第二次線上門禁系統資安 標準制定草案小組 2021/09/16線上 安防AI技術 與解決方案實務應用交流會 2021/09/23 第一次門禁系統資安標準 制定專家審查會議 2021/09/24 興創知能產業AI落地實證 與擴散會議 2021/10/04 昇銳電子產業AI 落地實證與擴散會議 2021/10/20 第二次門禁系統資安標準 制定專家審查會議 2021.1223安防產業趨勢發展研討會 謝君偉-人工智慧新發展應用於安防之未來 趙緝熙-建構資安的個資問題探討 2021.1223安防產業趨勢發展研討會 1/11 2021/12/23 安防產業趨勢發展研討會 1/10 2022/06/10 門禁系統資安標準認證-輔導 講座【第一部：一般要求】 S__59613410 S__59613409 20200807-第一次 S__59613410 1/14 2020 0814 Add a Title Describe your image Add a Title Describe your image Add a Title Describe your image Add a Title Describe your image 1/4 魔幻沙漠 摩洛哥 4/11-5/12 $600 魔幻沙漠 摩洛哥 4/11-5/12 $600 魔幻沙漠 摩洛哥 4/11-5/12 $600 魔幻沙漠 摩洛哥 4/11-5/12 $600 魔幻沙漠 摩洛哥 4/11-5/12 $600 魔幻沙漠 摩洛哥 4/11-5/12 $600 魔幻沙漠 摩洛哥 4/11-5/12 $600 魔幻沙漠 摩洛哥 4/11-5/12 $600 魔幻沙漠 摩洛哥 4/11-5/12 $600 魔幻沙漠 摩洛哥 4/11-5/12 $600 魔幻沙漠 摩洛哥 4/11-5/12 $600 魔幻沙漠 摩洛哥 4/11-5/12 $600

< Back 加拿大進口法規及程序簡介 2023年2月8日 上午9:14:32 資料來源： 駐加拿大代表處經濟組 我國與加拿大近年經貿交流熱絡帶動我商積極拓展加國市場，為協助我商掌握商機， 我國駐加拿大代表處經濟組綜整加國貿易法規、進口準備文件、通關流程等簡要說明，並蒐集產品進口之加國主管部門、法規與標準以及搜尋引擎等政府公開資訊， 俾利我商出口產品至加拿大前，了解該產品可能涉及的主管機關及進口與管理法規。 加拿大進口法規及程序簡介_(20230106更新) .pdf 下載 PDF • 272KB 加拿大產品進口與管理法規、主管部門及搜尋引擎_(20230206更新) .pdf 下載 PDF • 161KB < Previous Next >

Facial recognition – fascinating and intriguing 2020-09-11 Thales Digital Communications 新聞來源： https://www.thalesgroup.com/en/markets/digital-identity-and-security/government/biometrics/facial-recognition Facial recognition – fascinating and intriguing In this web dossier, you'll discover the seven face recognition facts and trends set to shape the landscape in 2020. But more about that later. In this web dossier, you'll discover the seven face recognition facts and trends set to shape the landscape in 2020. Top technologies and providers AI impact - Getting better all the time 2019-2024 markets and dominant use-cases Face recognition in China, India, United States, EU, and the UK, Brazil, Russia... Privacy vs Security: laissez-faire or freeze, regulate or ban? Latest hacks: can facial recognition be fooled? Moving forward: towards hybridized solutions. Let’s jump right in. How facial recognition works Facial recognition is the process of identifying or verifying the identity of a person using their face. It captures, analyzes, and compares patterns based on the person's facial details. The face detection process is an essential step as it detects and locates human faces in images and videos. The face capture process transforms analogue information (a face) into a set of digital information (data) based on the person's facial features. The face match process verifies if two faces belong to the same person. Today it's considered to be the most natural of all biometric measurements. And for a good reason – we recognize ourselves not by looking at our fingerprints or irises, for example, but by looking at our faces. Thales has specialized in biometric technologies for almost 30 years. The company has always collaborated with the best players when it comes to research, ethics, and biometric applications. Face match Before we go any further, let's quickly define two keywords: "identification" and "authentication". Face recognition data to identify and verify Biometrics are used to identify and authenticate a person using a set of recognizable and verifiable data unique and specific to that person. For more on biometrics definition, visit our web dossier on biometrics. Identification answers the question: "Who are you?" Authentication answers the question: "Are you really who you say you are?" Stay with us. Here are some examples : In the case of facial biometrics, a 2D or 3D sensor "captures" a face. It then transforms it into digital data by applying an algorithm before comparing the image captured to those held in a database. These automated systems can be used to identify or check the identity of individuals in just a few seconds based on their facial features: spacing of the eyes, bridge of the nose, the contour of the lips, ears, chin, etc. They can even do this in the middle of a crowd and within dynamic and unstable environments. Proof of this can be seen in the performance achieved by Thales' Live Face Identification System (LFIS), an advanced solution resulting from our long-standing expertise in biometrics. Owners of the iPhone X have already been introduced to facial recognition technology. However, the Face ID biometric solution developed by Apple was heavily criticized in China in late 2017 because of its inability to differentiate between individual Chinese faces. Of course, other signatures via the human body also exist, such as fingerprints, iris scans, voice recognition, digitization of veins in the palm, and behavioural measurements. Why facial recognition, then? Facial biometrics continues to be the preferred biometric benchmark. That's because it's easy to deploy and implement. There is no physical interaction required by the end-user. Moreover, face detection and face match processes for verification/identification are speedy. Best face recognition software So, what is the best face recognition software? #1 Top facial recognition technologies In the race for biometric innovation, several projects are vying for the top spot. Google, Apple, Facebook, Amazon, and Microsoft (GAFAM) are also very much in the mix. All the software web giants now regularly publish their theoretical discoveries in the fields of artificial intelligence, image recognition, and face analysis in an attempt to further our understanding as rapidly as possible. There's more. The very latest results of tests conducted in March 2018 and published in May by the US Homeland Security Science and Technology Directorate, known as the Biometric Technology Rally, also provide an excellent indication of the best face recognition software available on the market. But let’s take a closer look : Academia The GaussianFace algorithm developed in 2014 by researchers at The Chinese University of Hong Kong achieved facial identification scores of 98.52% compared with the 97.53% achieved by humans. An excellent rating, despite weaknesses regarding memory capacity required and calculation times. Facebook and Google Again in 2014, Facebook announced the launch of its DeepFace program, which can determine whether two photographed faces belong to the same person, with an accuracy rate of 97.25%. When taking the same test, humans answer correctly in 97.53% of cases, or just 0.28% better than the Facebook program. In June 2015, Google went one better with FaceNet. On the widely used Labeled Faces in the Wild (LFW) dataset, FaceNet achieved a new record accuracy of 99.63% (0.9963 ± 0.0009). Using an artificial neural network and a new algorithm, the company from Mountain View has managed to link a face to its owner with almost perfect results. This technology is incorporated into Google Photos and used to sort pictures and automatically tag them based on the people recognized. Proving its importance in the biometrics landscape, it was quickly followed by the online release of an unofficial open-source version known as OpenFace. Microsoft, IBM, and Megvii A study done by MIT researchers in February 2018 found that Microsoft, IBM, and China-based Megvii (FACE++) tools had high error rates when identifying darker-skin women compared to lighter-skin men. At the end of June 2018, Microsoft announced in a blog post that it had made substantial improvements to its biased facial recognition technology. Amazon In May 2018, Ars Technica reported that Amazon is already actively promoting its cloud-based face recognition service named Rekognition to law enforcement agencies. The solution could recognize as many as 100 people in a single image and can perform face match against databases containing tens of millions of faces. In July, Newsweek reported that Amazon’s facial recognition technology falsely identified 28 members of US Congress as people arrested for crimes. Key biometric matching technology providers At the end of May 2018, the US Homeland Security Science and Technology Directorate published the results of sponsored tests at the Maryland Test Facility (MdTF) done in March. These real-life tests measured the performance of 12 face recognition systems in a corridor measuring 2 m by 2.5 m. Thales' solution utilizing a Facial recognition software (LFIS) achieved excellent results with a face acquisition rate of 99.44% in less than 5 seconds (against an average of 68%), a Vendor True Identification Rate of 98% in less than 5 seconds compared with an average 66%, and an error rate of 1% compared with an average 32%. Face tracking March 2018 – The live testing done using more than 300 volunteers identified the best-performing facial recognition technologies. More on performance benchmarks: The NIST (National Institute of Standards and Technology) report, published in November 2018, details recognition accuracy for 127 algorithms and associates performance with participant names. The NIST Ongoing Face Recognition Vendor Test (FRVT) 3 performed at the end of 2019 provides additional results. See NIST report. NIST also demonstrated that the best facial recognition algorithms have no racial nor sex bias, as reported in January 2020 by ITIF. Critics were wrong. Mid-June 2020, IBM said it will no longer offer facial recognition technology and stop its research and development activities, and Microsoft pulled its face recognition solutions from law enforcement agencies in the United States. In a blog post published on 10 June, Amazon is putting a moratorium of one year on the use of its technology by police. The e-commerce giant said it’s giving time for federal laws to be initiated and protect human rights and civil liberties in this domain. Facial emotion detection and recognition Emotion recognition (from real-time of static images) is the process of mapping facial expressions to identify emotions such as disgust, joy, anger, surprise, fear, or sadness on a human face with image processing software. Its popularity comes from the vast areas of potential applications. It's different from facial recognition which goal is to identify a person, not an emotion. Face expression may be represented by geometric or appearance features, parameters extracted from transformed images such as eigenfaces, dynamic models, and 3D models. Providers include Kairos (face and emotion recognition for brand marketing), Noldus, Affectiva, Sightcorp, Nviso, among others. #2 Learning to learn through deep learning The feature common to all these disruptive technologies is known as Artificial Intelligence (AI) and, more precisely, deep learning where a system is capable of learning from data. Why is it important? It's a central component of the latest-generation algorithms developed by Thales and other key players in the market. It holds the secret to face detection, face tracking, and face match as well as real-time translation of conversations. The result? Face recognition systems are getting better all the time. According to a recent NIST report, massive gains in accuracy have been made in the last five years (2013- 2018) and exceed improvements achieved in the 2010-2013 period. Most of the face recognition algorithms in 2018 outperform the most accurate algorithm from late 2013. In its 2018 test, NIST found that 0.2% of searches, in a database of 26.6 million photos, failed to match the correct image, compared with a 4% failure rate in 2014. Yes, you read that right. It's a 20x improvement over four years. Think about it this way: Artificial neural network algorithms are helping face recognition algorithms to be more accurate. #3 Facial recognition markets Face recognition markets A study published in June 2019, estimates that by 2024, the global facial recognition market would generate $7 billion of revenue, supported by a compound annual growth rate (CAGR) of 16% over the period 2019-2024. For 2019, the market is estimated at $3.2 billion. The two most significant drivers of this growth are surveillance in the public sector and numerous other applications in diverse market segments. According to the study, the top facial recognition vendors include : Accenture, Aware, BioID, Certibio, Fujitsu, Fulcrum Biometrics, Thales, HYPR, Idemia, Leidos, M2SYS, NEC, Nuance, Phonexia, and Smilepass. The main facial recognition applications can be grouped into three principal categories. What is facial recognition used for? Here are the top three application categories where facial recognition is being used. 1. Security - law enforcement This market is led by increased activity to combat crime and terrorism. The benefits of facial recognition systems for policing are evident: detection and prevention of crime. Facial recognition is used when issuing identity documents and, most often combined with other biometric technologies such as fingerprints (prevention of ID fraud and identity theft). Face match is used at border checks to compare the portrait on a digitized biometric passport with the holder's face. In 2017, Thales was responsible for supplying the new automated control gates for the PARAFE system (Automated Fast Track Crossing at External Borders) at Roissy Charles de Gaulle airport in Paris. This solution has been devised to facilitate evolution from fingerprint recognition to facial recognition during 2018. Face biometrics can also be employed in police checks, although its use is rigorously controlled in Europe. In 2016, the "man in the hat" responsible for the Brussels terror attacks was identified thanks to FBI facial recognition software. The South Wales Police implemented it at the UEFA Champions League Final in 2017. In the United States, 26 states (and probably as many as 30) allow law enforcement to run searches against their databases of driver’s license and ID photos. The FBI has access to driver’s license photos of 18 states. Drones combined with aerial cameras offer an interesting combination for facial recognition applied to large areas during mass events, for example. According to the Keesing Journal of Documents and Identity of June 2018, some hovering drone systems can carry a 10-kilo camera lens that can identify a suspect from 800 meters from a height of 100 meters. As the drone can be connected to the ground via a power cable, it has an unlimited power supply. The communication to ground control can’t be intercepted as it also uses a cable. Facial recognition CCTV systems can improve performance in carrying public security missions. Let's illustrate this with four examples: Find missing children and disoriented adults Identify and find exploited children Identify and track criminals Support and accelerate investigations facial recognition cctv 1. Find Missing children and disoriented adults. Face recognition CCTV systems can significantly accelerate operators’ efforts by enabling them to add a reference photo provided by the missing child’s parents and match it with past appearances of that face captured on video. Police can use face recognition to search video sequences (aka video analytics) of the estimated location and time the child has been declared missing. Police officers can better figure out the child’s movements before going missing and locate where he/she was last seen. A real-time alert can trigger an alarm whenever there's a match. Police can then confirm its accuracy and do what's necessary to recover the missing children. The same process can be applied for disoriented missing adults (e.g. with dementia, amnesia, epilepsy, or Alzheimer’s disease). 2. Identify and find exploited children. Isolating the appearances of specific individuals in a video sequence is critical. It can accelerate investigators’ jobs in child exploitation cases as well. Video analytics can help build chronologies, track activity on a map, reveal details and discover non-obvious connections among the players in a case. 3. Identify and track criminals. Face recognition CCTV can be used to enable police to track and identify past criminals suspected of perpetrating an additional infraction. Police can also take preventive actions. By using an image of a known criminal from a video or an external picture (or a database), operators can use to detect matches in live video and react before it’s too late. 4. Support and accelerate investigations. Facial recognition CCTV systems can be used to support investigators searching for video evidence in the aftermath of an incident. The ability to isolate the appearances of suspects and individuals is critical for accelerating investigators’ review of video evidence for relevant details. They can better understand how situations developed. 2. Health Significant advances have been made in this area. Thanks to deep learning and face analysis, it is already possible to: track a patient's use of medication more accurately detect genetic diseases such as DiGeorge syndrome with a success rate of 96.6% support pain management procedures. face analysis for health 3. Marketing and retail This area is undoubtedly the one where the use of facial recognition was least expected. And yet quite possibly it promises the most. Know Your Customer (KYC) is sure to be a hot topic in 2020. This important trend is being combined with the latest marketing advances in customer experience. By placing cameras in retail outlets, it is now possible to analyze the behavior of shoppers and improve the customer purchase process. How exactly? Like the system recently designed by Facebook, sales staff are provided with customer information taken from their social media profiles to produce expertly customized responses. The American department store Saks Fifth Avenue is already using such a system. Amazon GO stores are reportedly using it. How long before the selfie payment? Since 2017, KFC, the American king of fried chicken, and Chinese retail and tech giant Alibaba have been testing a face recognition payment solution in Hangzhou, China. #4 Mapping of new users While the United States currently offers the largest market for face recognition opportunities, the Asia-Pacific region is seeing the fastest growth in the sector. China and India lead the field. Face recognition in China Face recognition technology is the new hot topic in China, from banks and airports to police. Now authorities are expanding the facial recognition sunglasses program as police are beginning to use them in the outskirts of Beijing. China is also setting up and perfecting a video surveillance network countrywide. Over 200 million surveillance cameras were in use at the end of 2018, and 626 million are expected by 2020. The facial recognition towers in Chinese cities are emblematic of this move. This is linked to the social credit system the Chinese government is developing. In the TOP 10 cities with most street cameras per person, Chongqing, Shenzhen, Shanghai, Tianjin, and Ji’nan are leading the pack. London is #6 and Atlanta #10, according to the Guardian of 2 December 2019. There's more. Chinese police are working with artificial intelligence companies such as Yitu, Megvii, SenseTime, and CloudWalk, according to The New York Times of 14 April 2019. China's ambitions in AI (and facial recognition technology) are high. The country aims to become a world leader in AI by 2030. Surprisingly, China provides strong biometric data protection against private entities AND increases government's access to personal information. This paradox is evidenced by privacy expert Emmanuel Pernot- Leplay in his report dated 27 March 2020. Facial recognition in Asia Facial recognition will be a significant topic for the 2020 Olympic Games in Tokyo (postponed to September 2021). This technology will be used to identify authorized persons and grant them access automatically, enhancing their experience and safety. In Sydney, face recognition is undergoing trials at airports to help move people through security much faster and in a safer way. In India, the Aadhaar project is the largest biometric database in the world. It already provides a unique digital identity number to 1.26 billion residents as of August 2020. UIDAI, the authority in charge, announced that facial authentication would be launched in a phased roll-out by September 2018. Face authentication will be available as an add-on service in fusion mode along with one more authentication factor like fingerprint, Iris, or OTP. India could also roll-out the world's most extensive face recognition system in 2020. The National Crime Records Bureau (NCRB) has issued an RFP inviting bids to develop a nationwide facial recognition system. According to the 160-page document, the system will be a centralized web application hosted at the NCRB Data Center in Delhi. It will be available for access to all the police stations. It will automatically identify people from CCTV videos and images. The Bureau states that it will help police catch criminals, find missing people, and identify dead bodies. Other large projects In Brazil, the Superior Electoral Court (Tribunal Superior Eleitoral) is involved in a nationwide biometric data collection project. The aim is to create a biometric database and unique ID cards by 2020, recording the information of 140 million citizens. In Africa, Gabon, Cameroon, and Burkina Faso have chosen Thales to meet the challenges of biometric identity to uniquely identify voters in particular. Russia's Central Bank has been deploying a countrywide program since 2017 designed to collect faces, voices, iris scans, and fingerprints. But the process is progressing very slowly according to the Biometricupdate website of 13 March 2019. The city of Moscow claims one of the world’s largest network of 160,000 surveillance cameras by the end of 2019 and are to be fitted with facial recognition technology for public safety. The roll-out started in January 2020. Russian law does not regulate non-consensual face detection and analysis. Biometric information #5 When face recognition strengthens the legal system The ethical and societal challenge posed by data protection is radically affected by the use of facial recognition technologies. Do these technological feats, worthy of science-fiction novels, genuinely threaten our freedom? And with it, our anonymity? EU and UK biometric data protection In Europe and the UK, the General Data Protection Regulation (GDPR) provides a rigorous framework for these practices. Any investigations into a citizen's private life or business travel habits are out of the question, and any such invasions of privacy carry severe penalties. Applicable from May 2018, the GDPR supports the principle of a harmonized European framework, in particular protecting the right to be forgotten and the giving of consent through clear affirmative action. This directive is bound to have international repercussions. Yes, you read it well. There's now one law for 500 million people. US biometric data protection landscape In America, the State of Washington was the third US state (after Illinois and Texas) to formally protect biometric data through a new law introduced in June 2017. California was the fourth state as of January 2020. The California Consumer Privacy Act (CCPA) passed in June 2018 and effective as of 1 January 2020 will have a serious impact for privacy rights and consumer protection not only for residents of California but for the whole nation as the law is frequently presented as a model for a federal data privacy law. In that sense, the CCPA has the potential to become as consequential as the GDPR. In July 2018, Bradford L. Smith, Microsoft’s president, compared the face recognition technology to products like medicines that are highly regulated, and he urged Congress to study it and oversee its use. In May 2019, US Rep. Alexandria Ocasio-Cortez voiced her "absolute" concerns in a recent Committee Hearing on facial recognition Technology (Impact on our Civil Rights and Liberties). More recently, a New York State law called the Stop Hacks and Improve Electronic Data Security (SHIELD) became effective 21 March 2020. It requires the implementation of a cybersecurity program and protective measure fro NY State residents. The act applies to businesses that collect the personal information of NY residents. With the act, New York now stands beside California. Facial recognition bans (San Francisco, Somerville, Oakland, San Diego, Boston...) Privacy and civil rights concerns have escalated in the country as face recognition gains traction as a law enforcement tool and, on 6 May 2019, San Francisco voted to ban facial recognition. It is the first ban of its kind on the use of face recognition. The anti-surveillance ordinance signed by San Francisco's Board of Supervisors bars city agencies, including San Francisco PD, from using the technology as of June 2019. Yes, this includes law enforcement. There's more. As reported by the Boston Globe of 27 June 2019, the Somerville City Council (Massachusetts) voted to ban the use of facial recognition, making the city the second community to take such a decision. Lather, rinse, repeat. On 16 July 2019, Oakland (California) took the same decision and became the third US city to ban the use of face recognition technology. It is interesting to note that the Oakland Police department is not using this technology and was not planning to use it. San Diego took the same decision at the end of December 2019 in advance of the new Californian law. This new law (Assembly Bill 215) about facial recognition and other biometric surveillance) specifically prohibits the use of police body cameras in California. The ban is in place for three years as of 1 January 2020. Since the San Francisco, Sommerville, Oakland, and now San Diego rulings, the debate gets louder in many cities and not only in the U.S. Portland (Oregon) is considering a ban for 2020. Early January, the vote has been put on hold until June, however. Portland could be the first city to extend it to private stores, airlines, and event venues. On 24 June 2020, Boston voted to ban the use of face surveillance technology by police as reported by Boston Herald. In Europe, at the end of August 2019, Sweden's Data Protection Authority decided to ban facial recognition technology in schools and fined a local high school (the first GDPR penalty in the country). How to better regulate emerging technologies? So... Should other cities or countries follow this example? Is the ban just a "pause button" to better assess risks? Is this a step backwards for public safety? Is there a policy vacuum? At which level? Stay tuned for the outcome of all these discussions as the US Congress is getting pressure from activists to ban the technology and from providers (see box below) to regulate. The EU Commission is planning to act on indiscriminate use of facial identifier technology. The new European Commission president Ursula von der Leyen wants a coordinated approach to the human and ethical implications of artificial intelligence. She has pledged to publish an AI legislation blueprint very soon. The very first draft of the European commission whitepaper is available online. The document mentions “a time-limited ban on the use of facial recognition by private or public actors in public spaces.” Again the questions of privacy, consent, and function creep (data collected for one purpose being used for another) are central to the debate. Find more on biometric data protection laws (EU, UK and US perspective) in our biometric data dossier. India and its national biometric identification scheme, Aadhaar In India, thanks to the Puttaswamy judgment delivered on 27 August 2017, the Supreme Court has enshrined the right to privacy in the country's constitution. This decision has rebalanced the relationship between citizen and state and posed a new challenge to the expansion of the Aadhaar project. The Indian government, however, approved the use of the country's biometric EID program by private entities on 28 February 2019. Rebound effect: the legal system and its professions get even stronger. As both ambassadors and guardians of data protection regulation, the post of data protection officer has become necessary for businesses and a much sought-after role. can face recognition be fooled #6 The rebels – facial recognition hackers Despite this technical and legal arsenal designed to protect data, citizens, and their anonymity, critical voices have still been raised. Some parties are concerned and alarmed by these developments. Some have taken actions. But can facial recognition be fooled? In Russia, Grigory Bakunov has invented a solution to escape the eyes permanently watching our movements and confuse face detection devices. He has developed an algorithm that creates special makeup to fool the software. However, he has chosen not to bring his product to market after realizing how easily criminals could use it. In Germany, Berlin artist Adam Harvey has come up with a similar device known as CV Dazzle. He is now working on clothing featuring patterns to prevent detection. The hyperface camouflage includes patterns in fabric, such as eyes and mouths, to fool the face recognition system. In late 2017, a Vietnamese company successfully used a mask to hack the Face ID face recognition function of Apple's iPhone X. However, the hack is too complicated to implement for large-scale exploitation. Around the same time, researchers from a German company revealed a hack that allowed them to bypass the facial authentication of Windows 10 Hello by printing a facial image in infrared. Forbes announced in an article from May 2018 that researchers from the University of Toronto have developed an algorithm to disrupt facial recognition software (aka privacy filter). In August 2020, the Verge detailed a "cloaking" app named Fawkes. The software imperceptibly distorts your selfies and other pics you may leave on social media. The tool is coming from the University of Chicago’s Sand Lab. In short, a user could apply a filter that modifies specific pixels in an image before putting it on the web. These changes are imperceptible to the human eye but are very confusing for facial recognition algorithms. The industry is working on anti-spoofing mechanisms, and two topics have been specifically identified by standardization groups : Make sure the captured image has been done from a person and not from a photograph (2D), a video screen (2D) or a mask (3D), (liveness check or liveness detection) Make sure that facial images (morphed portraits) of two or more individuals have not been joined into a reference document, such as a passport. #7 Further together – towards hybridized solutions The identification and authentication solutions of the future will borrow from all aspects of biometrics. This will lead to "biometrix" or a biometric mix capable of guaranteeing total security and privacy for all stakeholders in the ecosystem. It's very much the spirit of Thales Gemalto IdCloud Fraud Prevention, a risk assessment, and fraud detection software for payments. In this solution, geolocation, IP-addresses (the device being used) and keying patterns can create a strong combination to authenticate users for on-line banking or egovernment services securely. This seventh trend belongs to us. It's our job to envisage it together and make it happen through high-added-value biometric projects. Face recognition and you Now it's your turn. The months to come hold many changes in store. Indeed, we can't claim to predict all the essential topics that will emerge in the short term future. Can you fill in some of the gaps? If you've something to say on face recognition, tech or trends, a question to ask, or have simply found this article useful, please leave a comment in the box below. We'd also welcome any suggestions on how it could be improved or proposals for future articles. We look forward to hearing from you. 關於中文翻譯，可參考3S Market https://3smarket-info.blogspot.com/2020/09/blog-post_73.html < Previous News Next News >

展望 2024 年：從挑戰中重新出發，迎向多元化的新世界 2023-12-18 TechNews 科技新報 新聞來源： https://technews.tw/2023/12/18/looking-ahead-to-2024/ 這一年，市場上彌漫著一種保守卻又充滿期待的氣氛，人們既小心翼翼，又充滿希望地期盼著 2024 年將帶來的變化。 當我們回首 2023 年，全球市場剛從疫情的陰霾中逐漸復甦。經歷了長時間的封鎖與停滯，全球經濟需要面對諸多挑戰，包括原物料的短缺、人力資源的重新配置，以及供應鏈的重建等問題。 消費者行為的轉變：從環境意識到品牌忠誠 在疫情之後，消費者對於品牌與環境之間的關係變得更加關注。企業社會責任和可持續發展目標（SDGs）成為了品牌建立聲譽和吸引消費者的重要因素。此外，消費者越來越傾向於選擇那些與自己價值觀一致的品牌，這使得品牌必須更加重視其獨特性和價值主張，以吸引不同消費取向的客戶。但同時市場也興起了一陣「漂綠風」，勢必也會讓消費者感到真假難辨，進而提高對社會公益形象的考核標準，品牌應真心致力於社會公益與環境永續，而非為了沽名釣譽而營造出虛偽的良善形象。 從全球市場的視角來看，未來國際集團品牌將透過更大量的數位媒體和廣告投放，來建立青少年消費者對品牌的早期認知，這部分也值得各國注意，全球化文化與反全球化浪潮將同步來襲，新一代消費者位處於矛盾與兩端的價值觀，將導致在相關的消費者洞察上更加困難。 數位技術與人工智慧的快速發展 隨著像ChatGPT這樣的人工智慧應用問世，我們見證了數位技術在疫情後的迅速發展。這些技術的應用範圍越來越廣泛，從數位廣告到電子信件和社交訊息，人工智慧與機器學習的速度，將顯著提升行銷效率和個性化體驗。然而，這也帶來了消費者可能對資訊過量感到不滿的風險，品牌如何在其中獲得平衡，將是未來客服客訴的關鍵，其中牽涉的個人隱私與資訊問題，更將是全球關注的重點。 短影音內容的崛起與消費者耐心的考驗 短影音內容的持續流行改變了人們的閱讀和觀看習慣。消費者逐漸失去對長篇內容的耐心，轉而偏好快速即時的獲取訊息，並從中立刻感受到多巴胺的快樂效用。品牌必須迅速適應這種變化，否則可能會面臨消費者的快速轉移和負面評價。 從另一個角度而言，市場即教育，未來人們也會因為內容偏好，導致注意力集中程度減損，品牌未來該如何溝通較為深度的品牌訊息，也將成為體驗行銷的一大考驗。 品牌需創新策略，機遇與挑戰並存 2024年預示著全球經濟與市場的新階段，在這個新的一年，品牌和企業將面臨諸多挑戰，包括適應消費者行為的轉變、應對數位技術的快速發展，以及維護在社交媒體上的良好形象。然而，這些挑戰也帶來了無限的機遇。品牌需要創新思維和靈活應變的策略，才能在這個變化迅速的市場中脫穎而出。 < Previous News Next News >

2024 年科技產業大預測：AI 加速駛向新世界 2023,-10-17 3C新報 新聞來源： https://ccc.technews.tw/2023/10/17/tf-annual-forecast-2023 全球市場研究機構 TrendForce 針對 2024 年科技產業發展，整理科技產業重點趨勢。 CSP增加AI投資，推升2024年AI伺服器出貨成長逾38% ChatBOT、生成式AI等各應用領域發力，CSP業者微軟、Google、AWS等增加AI投資，推升AI伺服器（AI Server）需求上揚，TrendForce估算，2023年AI伺服器（含搭載GPU、FPGA、ASIC等）出貨量逾120萬台，年增達37.7%，占整體伺服器出貨量達9%，2024年再成長逾38%，AI伺服器占比逾12%。除了NVIDIA與AMD的GPU解決方案，大型CSP業者擴大自研ASIC晶片將成趨勢，Google自下半年加速自研TPU導入AI伺服器，年成長逾七成，2024年AWS亦擴大採用自研ASIC，出貨量有望翻倍成長，其他微軟、Meta等亦規劃擴展自研ASIC計畫，GPU成長潛力因此受侵蝕。整體而言，2023~2024年主由CSP等業者積極投資帶動AI伺服器需求成長，2024年後延伸至更多應用領域業者投入專業AI模型及軟體服務開發，帶動搭載中低階GPU（如L40S等系列）等邊緣AI伺服器成長，2023~2026年邊緣AI伺服器出貨平均年成長率逾二成。 HBM3e推升全年HBM營收年增達172% AI伺服器（AI Server）建置熱潮帶動AI加速晶片需求，高頻寬記憶體（HBM）為加速晶片關鍵性DRAM產品。以規格言，除了市場主流HBM2e，今年HBM3需求比重亦隨NVIDIA H100 / H800及AMD MI300系列量產提升。展望2024年，三大記憶體廠商將推出新高頻寬記憶體HBM3e，速度提升至8Gbps，提供2024~2025年新AI加速晶片更高性能表現。AI加速晶片市場除了伺服器GPU龍頭廠商NVIDIA、AMD，CSP業者也加速開發自研AI晶片，產品共通點為皆搭載HBM。隨訓練模型與應用複雜性增加，帶動HBM需求大幅成長。HBM比其他DRAM產品平均單位售價高數倍，2024年將對記憶體原廠的營收顯著挹注，2024年HBM營收年增長率將達172%。 AI晶片幕後推手，2024年先進封裝需求增，3D IC技術萌芽 半導體前段製程微縮逼近物理極限，先進製程領導廠商台積電（TSMC）、三星（Samsung）及英特爾（Intel）除了尋求電晶體架構轉變，封裝技術演進也成為提升晶片效能、節省硬體使用空間、降低功耗及延遲的必要發展。台積電及三星更先後在日本建立3D IC研發中心，突顯封裝對半導體技術的重要性。近年Chatbot興起帶動AI應用蓬勃發展，協助整合運算晶片及記憶體，提供AI強大算力的2.5D封裝技術需求也隨之大增。2.5D封裝主要透過前段製程提供矽中介層（Silicon Interposer），將數個不同功能及製程的晶片以並排整合，再與PCB基板結合封裝。台積電CoWoS、英特爾EMIB、三星I-Cube等2.5D封裝皆發展數年，技術發展趨成熟並廣泛用於高效能晶片。2024年各廠將致力提高2.5D封裝產能以滿足日漸升溫的AI等高算力需求，3D封裝技術也萌芽。台積電SoIC、三星X- cube及英特爾FOVEROS皆陸續發表，與2.5D封裝差異在3D封裝去除矽中介層， 將不同功能晶片以TSV（矽穿孔）直接連接，降低封裝高度、縮短晶片傳輸路徑、提高晶片運算速度。 除此之外，不同功能及製程的晶片如何有效整合以達到AI、 自駕車等高算力、低延遲、低功耗需求，除了封裝技術突破，晶片連接方式甚至連接材料， 都是發展重點。 2024年全球非地面網路啟動小規模商用測試，加速非地面網路應用普及 全球衛星營運商Starlink與OneWeb衛星部署數量穩定增加，加上3GPP Release17與Release 18提供5G新空中介面（New Radio）在非地面網路發展方向，讓衛星商、晶片廠、電信商與手機商共同合作完成初步非地面網路（NTN）場景驗證。現階段非地面網路主要聚焦在行動衛星通訊應用領域，由用戶終端設備（UE）與衛星，測試特定場景雙向數據傳輸。展望2024年，晶片大廠加速推出衛星通訊晶片趨勢下，帶動手機大廠以系統單晶片（SoC）模式將衛星通訊功能整合至高階手機，部分用戶對高階手機有穩定需求，讓非地面網路朝小規模商用測試發展，成為2024年加速非地面網路應用普及驅動因素。從行動衛星通訊長期發展趨勢看，衛星間雷射光鏈（Inter Satellite Link，ISL）通訊技術能在低軌衛星間傳輸數據資料，並同時傳送至大規模跨區域用戶終端設備，實現6G低延遲全域通訊願景。 2024年6G通訊規劃啟動，衛星通訊扮演關鍵角色 6G標準化規劃2024~2025年啟動， 首個標準技術2027~2028年推出，6G關鍵技術突破，除納入超寬頻（Ultra- Wideband）接收器（Receiver）和發射器（Transmitter），地面和非地面網路整合、人工智慧與及機器學習將引入更多創新。6G將增加新技術應用，包括可重構智慧表面技術（RIS）、太赫茲頻段、光無線通訊（Optical Wireless Communication，OWC）、非地面網路實現高空通訊應用（NTN），沉浸式延展實境（XR）等更細緻感官體驗，透過創新提供殺手級應用，如全像投影（holographic）和觸覺通訊（Tactile Communications）、數位孿生等。6G技術標準逐次敲定，低軌衛星將陸續支援6G通訊，全球低軌衛星部署會在6G商用前後達高峰，估計應6G通訊、環境感測的無人機需求6G時代顯著提高。 更多新創業者加入，2024年Micro LED技術成本有望最佳化 2023年是Micro LED顯示技術邁入量產的關鍵年，解決成本居高不下問題是接下來首要之務。晶片部分，微型化工程啟動，大型顯示器主流34×58um開始被20×40µm甚至更小16×27µm取代。僅透過晶片微縮，四年內Micro LED晶片成本降幅每年至少20%~25%。轉移是Micro LED製程核心，Stamp製程穩定，雷射則是速度（Unit per Hour，UPH）取勝。邁入量產後， 業界著眼效率與良率取得更好平衡點，以Stamp轉移搭配雷射鍵合的混合轉移模式，冷加工概念能有效解決Stamp熱壓合的壓力與溫度問題，為備受關注的生產模式。 AR透明顯鏡微投影顯示是Micro LED極具潛力的應用市場，因極高PPI（Pixel per Inch）要求，尺寸必須控制在5µm甚至更小，相伴的晶片外部量子效率（EQE）低落問題也更棘手。採紅、藍、綠三色LED方案雖然單純，但紅光效率低落難克服。以藍光LED搭配量子點材料色轉換雖然有效迴避，但衍生出額外製程與材料壽命問題。新創企業跳脫傳統切入點，InGan基底的紅光LED、RGB LED垂直堆疊等方案也同樣引人注目。即使還難判斷哪個技術路線會成主流，但百家爭鳴有利催生最佳解決方案。零組件改善、製程最佳化、豐富解決方案，量產與應用多元化吸引下，2024年將有更多廠商投入，健全供應鏈同時，也最佳化Micro LED成本架構。 AR / VR不同微型顯示發展與競爭更激烈 AR / VR等頭戴裝置需求帶動，具超高PPI近眼顯示器需求提升，Micro OLED顯示器正是代表技術。雖然正式採用Micro OLED顯示器的AR / VR裝置不多，但關鍵品牌客戶採用後，Micro OLED顯示器就有機會逐步擴大規模。未來個人化顯示器持續發展，微縮化趨勢逐漸成形，必須仰賴半導體製程與顯示技術整合，不同的微型顯示技術如Micro LED也持續發展。Micro OLED顯示器將是集半導體製程與AMOLED蒸鍍製程之大成，對Micro OLED面板廠商而言，能否取得穩定晶圓代工資源搭配是一大關鍵。新廠商與既有廠商產業資源重新盤整是現在進行式，搭配OLED技術也有望從白光OLED逐漸朝RGB OLED發展。不過Micro OLED顯示器仍有瓶頸，如亮度及發光效率限制，能否取得頭戴裝置主流地位，仍需觀察各微型顯示技術發展進程。 材料與元件並進，氧化鎵商業化腳步漸近 高壓、高溫、高頻等應用場景增加，氧化鎵（Ga₂O₃）為超寬禁帶半導體材料，認為是下一代功率半導體元件的有力競爭者，特別電動車、電網系統、航空航太等領域。相較氣相生長的碳化矽與氮化鎵，氧化鎵單晶製備可透過類似矽單晶熔融生長法，擁有較大降本潛力。產業界已實現4吋氧化鎵單晶量產，有望幾年內擴大至6吋。基於氧化鎵材料的肖特基二極體與電晶體結構設計、製程等亦取得突破性進展，首批肖特基二極體產品2024年投放市場，有望成為首個規模商用的氧化鎵功率元件。即使氧化鎵仍有導熱性差與P型摻雜缺失等棘手挑戰，但相信功率半導體巨頭跟進及關鍵應用牽引，商業化指日可待。 動力電池或加速進入新電池更新，固態電池決定下個十年格局 全球動力電池產業進入TWh智造時代，對高安全與高能量密度電池的需求更突出，主流動力電池技術路線都近能量密度天花板，現有材料體系對電池能量密度與安全性等提升不足以滿足市場需求。各大車廠與電池廠商加速電池投資與研發，技術將迎接新突破，兼顧更高能量密度和安全性的固態電池技術成為各大企業重點，產業化更深入探索和實踐，包括凝聚態電池等半固態電池技術，開發和商業化應用或2024年加速動力電池產業進入更新，並對下個十年動力電池產業新格局產生重要影響。 鋰離子電池在電動車領域地位明確，但車輛類型眾多且用途情境相異，不同電池技術各因優勢存在。鈉離子電池因鈉元素儲量大且分布均勻有低成本優勢，但能量密度也低，故適合打造續航力不敏感的低價電動車，中國電池廠正致力產業化。氫燃料電池主打零排放、長續航、加氫速度快和支援冷啟動，重型商用車是重點類別，但氫燃料電池尚有能源轉換效率低、製氫及儲運成本高、製氫材料來源具爭議等問題，加上產業成熟度不足，市場乘用和商用車款仍少，需長續航的重型卡車大規模商用時間預計2025年後。 提高能源轉換效率、續航力、充電效率是2024年純電動車三大核心 從能源轉換效率看， 低損耗SiC晶片是提高BEV能源轉換效率的關鍵零件，2024年SiC 8吋晶圓產能將逐漸釋放，但良率仍待加強且多數產能已被下游廠商鎖定，晶片成本降幅有限，晶片端縮小尺寸目標推動下，更進一步提高「溝槽型晶片技術」研發投入程度。 續航力方面，NCM（三元鋰電池）及LFP（磷酸鐵鋰電池）仍為車廠首選，最佳化電池包結構、調整材料配比以提高能量密度、增加續航力為目標；高能量密度的固態電池將先以半固態電池下半年開始少量裝車，2024年是觀察商業化的關鍵點。充電效率方面，為縮短充電時間，800V平台車型明顯增加，可支援360kW以上高功率快充，高功率快充站建設熱潮也隨之而起。無線充電進展加快，美國提出電動車無線充電補助法案，密西根州將開放總長1.6公里無線充電公路，充電方式朝多元發展，可望降低車主里程焦慮。 蓬勃發展的AI則協助電動車朝高度自動駕駛邁進，自動駕駛系統開發，可靠度是判斷是否進入市場的關鍵，AI扮演提高效率角色，協助巨量圖像分類與標記、搭建仿真模擬場景。隨著其他車廠急起直追，特斯拉Dojo超級電腦宣布量產，並計畫2024年投資10億美元以Dojo訓練神經網路，領先競爭者更先進自駕系統、制定可負擔售價將是特斯拉智慧駕駛站穩地位的利器。 全球加強綠化，AI模擬成推動再生能源與脫碳製造關鍵 國際能源署（IEA）指出，2024年全球再生能源發電量有望達4,500GW，近乎化石燃料，主要是政策推廣力道強化、化石燃料價格上漲、戰爭造成能源危機等。再生能源能發電若要穩定，電網、儲能、 管理等周邊系統勢必以AI加速智慧化並提升緩衝空間與精確度。以智慧電網為例，監督式學習（Supervised Learning）最佳化電力輸入輸出、非監督式學習（Unsupervised Learning）改善數據擷取品質，以及負載預測（Load Forecasting）、穩定性評估等強化整體效益，皆是2024年能源綠化技術發展關鍵。2024年智慧製造與能源管理將聚焦驅動系統能耗最佳化、全數據串連生態圈、可視化能源流動消費，藉動態數位孿生（Dynamic Digital Twin）虛實整合，將數據從碳流轉為綠流，再化為金流。生成式AI、3D列印等能加速製造設計、生產建模等環節，減少資源浪費，後勢頗具潛力。綜觀各領域綠化訴求，組織先須瞭解自家排碳量與碳足跡，碳盤查工具成雲端大廠重點產品，並持續以AI與機器學習最佳化碳排放量。 摺疊手機引領創新，新技術材料商業化推動OLED產業拓展小到大應用 OLED摺疊手機不斷創新，成功製造市場話題後，新上市摺疊手機無不針對消費者期望大幅改善，如更換輕量化複合材料給門板及螢幕支撐板，一體成形水滴型鉸鏈結構有效減少零件數量，甚至利用機殼蓋板取代鉸鏈龍骨，步步逼近直板機厚度與重量。當摺疊手機滲透率逐漸提升，除了不斷技術推演，還需要有效降低成本，市場普及時還能確保利潤。隨著OLED手機市場滲透擴大，IT將是下個OLED關鍵戰場。為了拓展IT市場滲透率，三星宣布啟動G8.7新廠投資計畫，京東方規劃中B16、JDI新技術eLEAP、維信諾朝OLED積極搶進，讓面板廠高世代佈局不僅因應蘋果中尺寸需求，也為OLED面板拓展其他應用市場開啟新契機。 2025年後新技術開發與導入將打破FMM及蒸鍍機台尺寸限制，加上高壽命材料商用化，高世代產線順利量產，均有助提升OLED各應用市場滲透率。 < Previous News Next News >

人才培訓講師介紹 開課資訊 課程花絮 台灣智慧安防公會與景文科技大學產學合作，協助已從業或即將投入就業之安防業者認識安防系統工程的各項內容及標準規範，藉此讓安防產業之工程品質再升級。 理論課程由景文科技大學安排資通訊及智慧科技專業領域講師群授課。 產業實務課程由安防公會聘請業界先進，專業傳授安防工程標準化架構與作業規範。 ※依單位筆畫排列 蔣繼陳 經理／台灣新光保全(股)公司 李日宇 副理／利凌企業(股)公司 范明翔 經理 ／昇銳電子(股)公司 范建芳 外銷業務經理／茂旭資訊(股)公司 廖文龍 內銷業務推廣經理／茂旭資訊(股)公司 羅福枝 總經理／晟福科技(股)公司 周伯毓 老師／景文科大資訊與通訊系 翁啟明 老師／景文科大資訊與通訊系 張明化 老師／景文科大資訊與通訊系 應誠霖 老師／景文科大資訊與通訊系 劉剛廷 資深經理／勤紘科技(股)公司 蔣繼陳 老師 台灣新光保全股份有限公司 / 經理 經歷： 維修工程師、電信工程師。 電信管理課專員、課長。 服務維修課襄理、技術管理部副理。 保全工程部經理。 大型專案經歷：惠康超市、家樂福街邊店、台新大樓總部、外交部門禁、保全系統規劃，各中小型連鎖施工規範。 范明翔 老師 昇銳電子股份有限公司 經歷： 中山科學研究院相列雷達組 尋標系統設計 與中科院其他系統組別偕同開發雄風二型雷達系統尋標軟體開發工作 昇銳電子 研發二部 錄影主機系統軟體設計 參與主機系統架構與遠端連線軟體規劃 昇銳電子 營業處 市場企劃部 了解市場需求，規劃系列產品因應市場需求，產出系統文案 廖文龍 老師 茂旭資訊股份有限公司 / 內銷業務推廣經理 經歷： 茂旭資訊股份有限公司 97年7月~至今 周伯毓 老師 景文科技大學資訊與通訊系 / 助理教授 經歷： 景文科技大學 電腦與通訊系助理教授(2021.2~至今) 景文科技大學 行動商務與多媒體應用系助理教授(2019.7~2021.1) 景文科技大學 行動商務與多媒體應用系助理教授兼系主任(2018. 8~2019.7) 景文科技大學 資管系助理教授 (2007.8~2018. 7) 　　兼研發處就業輔導組組長(2013.2~2015 7) 　　兼進修部推廣教育中心主任(2007.8~2011.7) 張明化老師 景文科技大學資訊與通訊系 / 副教授 經歷： 景文科技大學 資訊與通訊系副教授 　　　　　　　　兼資訊與通訊系 系主任 景文科技大學 電腦與通訊系副教授 　　　　　　　　兼創新育成中心主任 　　　　　　　　兼研發處產學組組長 　　　　　　　　兼資工系系主任 　　　　　　　　兼環物系系主任 劉剛廷 老師 勤紘科技股份有限公司 / 資深經理 經歷： 擁有超過 10 年網路技術與資安顧問經驗，專長於網路產品推廣、Pre-Sales 技術支援及解決方案規劃。熟悉多品牌設備如 Zyxel、Fortinet、Palo Alto、Aruba 等，並具備豐富 PM 團隊與技術團隊管理經驗。持有 CISM、CISSP、CCNP 等專業證照，能從技術、市場、策略三方協助企業拓展市場與落實資安與網路升級目標。 李日宇 老師 利凌企業股份有限公司 / 副理 經歷： 利凌技術支援部／副理 創益科技維護部／主任 兆豐ATM 監控系統汰舊換新案／ 專案PM 山隆自助加油站監控系統建置案／專案PM 蘇澳港運輸改善CCTV 專案／專案PM 三峽國光社宅CCTV建置／專案PM 統一超商CCTV專案／專案PM 台東科技執法專案／專案PM 范建芳 老師 茂旭資訊股份有限公司 / 外銷業務經理 經歷： 茂旭資訊股份有限公司 98年8月~至今 羅福枝 老師 晟福科技股份有限公司 / 總經理 經歷： 晟福科技 總經理 TW 鐿創科技 大中國區協理 TW 聚碩科技 工程部, 協理 TW 精誠科技 工程部, 經理 TW 翁啟明 老師 景文科技大學資訊與通訊系 / 助理教授 經歷： 景文科技大學助理教授 景文科技大學育成中心主任 前程科技公司顧問 禾翔通信有限公司研發課長 研華科技公司高級工程師 應誠霖 老師 景文科技大學資訊與通訊系 / 教授 經歷： 現任景文科技大學資訊與通訊系教授，曾任系主任、產學組組長，教授物理、專業數學、電磁學、光纖通信、科技英文等課程，擁有專業證照12張，指導學生曾獲榮獲俄羅斯阿基米德、IWA摩洛哥、馬來西亞MTE及IIIC國際發明展等國際發明展大獎。 景文科技大學 資訊與通訊系 教授(2023~迄今) 景文科技大學 電腦與通訊系 教授(2018~2023) 景文科技大學 資訊工程系 教授兼系主任(2019~2021)