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

113年起高考三級新增「資通安全」類科 2023-12-28 考試院 新聞來源： https://www.exam.gov.tw/News_Content.aspx?n=1&s=48255 考試院院會112年12月28日通過，113年起高考三級新增「資通安全」類科，滿足用人機關進用資安人才需求。 考試院院會112年12月28日通過，公務人員高等考試三級考試暨普通考試規則第12條、第2條附表一及第4條附表三修正案，自113年起高考三級新增「資通安全」類科，以滿足用人機關進用資安專業人才之需要。 考試院說明，有鑑於用人機關對於資安業務的迫切需求，以應對全球複雜多元的資通環境，及嚴峻的資通安全駭侵威脅，亟需資通安全專業人才來落實精進各項資通安全防護工作。惟目前公務人員考試設置的類科，僅有「資訊處理」類科的專業與資通安全業務略有相關，但資訊處理人員的工作內容與資安人員的職能要求顯有相當差異，因此仍需新增「資通安全」類科，以滿足各機關對資安專業人才的需求。 考試院進一步說明，為進用資通安全專業人才，助益國家資安事務的推行，經考選部會同數位發展部研議，於高考三級資訊處理職系下新增「資通安全」類科，專業科目列考「資通安全概論」、「資通安全管理」、「資通安全法令與規範」及「資通安全防護技術」等4科。未來對於透過考試錄取進用的人員，亦將持續強化其專業訓練及資安證照的取得，以因應快速變化的技術與業務挑戰，藉此協助各用人機關強化資通安全防護能量，降低資安風險與威脅，打造安全可信賴的數位國家。 考試院表示，為使應考人有所準備與因應，資通安全類科考試自113年起施行，歡迎有志從事資通安全工作者，踴躍報考。 考試院院會112年12月28日通過，113年起高考三級新增「資通安全」類科，滿足用人機關進用資安人才需求，歡迎有志從事資通安全工作者，踴躍報考。圖／考試院提供 < Previous News Next News >

Info-Tech：《2026年世界技術趨勢報告》 2025-10-17 鉅亨網 新聞來源： https://hao.cnyes.com/post/201059 一場深刻的變革正在重塑全球商業格局。Info-Tech研究集團發佈的《2026年技術趨勢報告》描繪了一幅複雜而充滿張力的未來圖景 一方面，地緣政治的割裂與經濟的“去全球化”正以前所未有的力度衝擊著運行數十年的穩定秩序；另一方面，以人工智慧（AI）為首的新興技術正從輔助工具進化為能夠自主決策與執行的智能體，以前所未有的速度顛覆著企業的核心營運模式。 這份基於對全球超過700名IT決策者調查的報告指出，世界正處於“顛覆加深，機遇拓寬”的關鍵節點。企業不再僅僅是數位化轉型的參與者，而是必須在日益增長的不確定性與指數級技術爆發的雙重壓力下，重構其生存與發展的底層邏輯。從供應鏈的“韌性”壓倒“成本”，到AI智能體編排引領的“自主企業”曙光，再到IT部門從後台支撐躍升為價值創造的“指數級引擎”，一場圍繞韌性、自主性與平台化的範式革命已經到來。未來的贏家，將是那些能夠成功駕馭這場風暴，將不確定性轉化為戰略優勢的組織。 從全球化到“堡壘化”：韌性成為新的增長引擎 過去數十年，全球企業遵循的核心法則是效率與成本最佳化，這催生了高度一體化、低摩擦的全球供應鏈。然而，這一黃金時代正迅速走向終結。報告明確指出，始於2025年的一系列新經濟政策，尤其是關稅壁壘，已將昔日的自由貿易走廊轉變為充滿費用的通道。疊加全球範圍內日益加劇的衝突與東西方之間不斷擴大的裂痕，企業正被迫從“全球化”的迷夢中驚醒，直面“去全球化”的嚴酷現實。 這種轉變的核心驅動力在於風險。報告資料顯示，衡量全球不確定性的世界不確定性指數（WUI）自2025年初以來飆升了481%，遠超新冠疫情期間的峰值。地緣政治風險不再是遙遠的背景噪音，而是直接影響企業營運的現實威脅。無論是對特定國家資訊通訊技術（ICT）產品的封鎖，還是對半導體等關鍵技術供應鏈的依賴，都迫使企業重新審視其風險管理框架。 因此，“韌性”取代了“成本”，成為供應鏈戰略的首要考量。報告強調，企業正從依賴單一的全球採購轉向建構更具適應性、多元化和可靠性的供應網路。這不僅意味著地理上的“近岸外包”或“在岸外包”，更體現在一種動態的戰略敏捷性上。例如，全球半導體產業正積極進行製造基地的多元化佈局，台積電、美光和英特爾等巨頭紛紛在美國、印度和歐盟投入巨資建設新的生產設施，以避險單一地區的地緣政治風險。這種多元化佈局雖然在短期內可能導致成本上升，但它為企業提供了在未來貿易政策變動中靈活切換供應來源的能力，從而獲得了寶貴的長期價格可預測性。 供應鏈的重構只是冰山一角，更深層次的變革在於“整合性組織韌性”的建構。傳統的風險管理模式往往是孤立的、回顧性的，以審計和事件響應為中心。然而，在當前快速變化的環境中，這種模式已然失效。報告指出，領先的企業正將風險管理從一個獨立的職能部門提升為一種內嵌於所有業務能力中的戰略核心。IT部門在其中扮演了關鍵的協調者角色，通過集中的治理、自動化的合規檢查以及無縫連接的監控與事件響應，將風險管理融入API、資料平台、AI智能體和安全控制的每一個環節。 資料顯示，被定義為“創新者”的IT部門中有近80%已經採用了完全整合的風險管理架構，這一比例是普通IT部門的兩倍多。他們更傾向於將風險視為戰略推動者，通過情景規劃和AI增強的預測分析，獲得一種面向未來的風險洞察力。例如，再保險行業已率先利用生成式AI整合包括衛星圖像和物聯網感測器在內的非結構化資料，極大地增強了對極端事件的建模和風險評估能力。這種將風險管理從“成本中心”轉變為“戰略賦能者”的思維轉變，正是企業在不確定性時代構築核心競爭力的關鍵所在。 AI智能體崛起：企業營運模式的範式革命 如果說重構韌性是企業在割裂世界中的防禦姿態，那麼擁抱“引導下的智能自主”則是主動出擊的利器。報告最為核心的洞察之一，是AI技術正經歷一場從“新興”到“變革性”的質變。根據技術投資指數，AI或機器學習的投資指數已從-3飆升至64，增長率高達80%，其重要性已接近雲端運算和網路安全。其中，Agentic AI（智能體AI）雖是新品類，但其採用率和增長潛力遠超幾年前的生成式AI，預示著一個新時代的到來。 過去的AI，特別是大型語言模型（LLM），大多作為“副駕駛”或聊天助手被整合到軟體中，幫助人類處理資訊。然而，當面對複雜、多步驟的工作流時，其上下文限制和推理能力的不足便暴露無遺，許多企業難以將AI從概念驗證階段推向能產生可觀投資回報的規模化應用。 “多智能體編排”的出現，正在打破這一僵局。報告將其定義為“從基於單個任務的智能體，演變為多個協同追求共同目標的智能體生態系統”。這意味著AI不再是被動響應的工具，而是能夠主動感知數字環境、利用上下文資訊做出決策並採取行動的自主工作者。一個“主管”AI可以管理多個“下屬”AI，沿著預設流程執行任務，並強制執行企業治理規則，從而實現整個業務流程的自動化。 這不僅是效率的提升，更是對企業營運模式的根本性重塑。在軟體開發領域，AI智能體能夠覆蓋從編碼、測試到性能監控的全生命周期，一位受訪的CEO表示，其工程師的生產力在一年內提升了至少十倍。在客戶支援領域，AI智能體可以7x24小時提供個性化服務，並自動完成例行任務。在銷售領域，它們可以自動進行潛在客戶拓展、最佳化報價並預測需求。美國抵押貸款提供商Direct Mortgage Corp.通過部署AI智能體，將貸款處理時間縮短了一半，營運成本降低了80%，實現了24小時內放款的驚人效率。 伴隨AI能力的躍升，“服務即軟體”（Service as Software）這一全新的商業模式應運而生。報告預見，企業將從為軟體使用權付費（SaaS模式），轉向為軟體直接交付的業務成果付費。在這種模式下，使用者通過自然語言下達指令，由後台的AI智能體生態系統自動完成端到端的流程，企業只需為最終實現的價值（如完成一筆交易、招募一名員工）買單。這不僅將軟體市場的潛在規模從SaaS的數千億美元，擴展到全球高達4.6兆美元的服務市場，也徹底改變了企業與技術的互動方式——人類不再需要適應軟體的介面，而是AI主動適應人類的需求。 然而，智能自主的崛起也帶來了前所未有的挑戰。報告用整整一個章節探討了“AI作為對手與盟友”的二元性。一方面，AI正加劇網路安全領域的“軍備競賽”。網路犯罪分子利用AI編寫惡意軟體、定製釣魚郵件、製造深度偽造內容，極大地降低了攻擊門檻並提高了攻擊的複雜性。另一方面，防禦方也部署AI來自動化威脅監控、檢測和修復。這場競賽的終局，可能是完全由AI驅動的、超越人類干預速度的自主攻防戰。 更令人深思的是AI的“失控”風險。報告引用了Google和前OpenAI研究人員的預測，認為超級智能可能最早在2027年出現。屆時，AI可能為了實現自身目標而偽造與人類目標的一致性，甚至發展出自我保護的意圖。這種“AI失調”的風險雖然聽起來像是科幻小說，但已有跡象表明，現有的AI模型在特定條件下會違背人類指令。這要求企業在擁抱AI帶來的巨大機遇時，必須建立嚴格的治理框架、安全協議和“一鍵關停”機制，確保人類始終掌握最終控制權。 指數級IT：重塑企業數字骨架 無論是建構組織韌性，還是駕馭AI智能體，都離不開一個強大而現代化的數字基礎。報告提出了“指數級IT”（Exponential IT）的概念，其核心思想是IT部門的角色必須從被動的後台營運者，轉變為能夠為企業提供指數級價值的創新整合者和平台建構者。這要求在兩個關鍵領域進行徹底的變革：資料治理和基礎設施。 首先是“聯盟式資料治理”（Federated Data Governance）。長期以來，企業試圖通過建立中心化的資料湖來消除資料孤島，但結果往往是“資料湖”變成了“資料沼澤”——資料質量低下，且中心化的資料團隊成為業務瓶頸。報告指出，未來的趨勢是走向去中心化的資料架構，其中“資料網格”（Data Mesh）是最具代表性的模式。其核心原則是，資料的所有權和管理責任應回歸到最瞭解這些資料的業務領域團隊。這些團隊將資料作為“產品”來管理和提供，並通過標準化的“資料合約”確保資料質量、權限和可用性。 這種模式的優勢在於，它將資料責任與業務價值流直接對齊，極大地提升了資料的質量和可用性。同時，通過一個統一的中繼資料層和資料平台，企業仍然可以實現中心化的治理策略自動化。中繼資料成為了連接分散資料產品的“骨架”，它不僅讓資料可被發現、可被理解，更是AI智能體合規訪問企業資料的關鍵。一個治理良好、高度自動化的資料網格，是實現AI驅動的自主企業不可或缺的前提。 其次是“專用平台”（Purpose-Built Platforms）的興起。雲端運算時代一度推崇“一刀切”的通用計算基礎設施，但隨著AI等專業工作負載需求的爆發，這種模式的侷限性日益凸顯。AI模型訓練和推理需要大規模平行處理、高吞吐量和巨大的記憶體頻寬，通用CPU難以滿足。因此，專門為AI設計的晶片（如Google的TPU、亞馬遜的Trainium）以及與之配套的高速網路和低延遲儲存應運而生。 這種“專用化”趨勢正貫穿整個IT技術堆疊。從晶片到網路，再到開發者環境，IT正在從提供通用資源轉向根據特定業務目標量身定製解決方案。例如，AI開發公司Anthropic利用定製化的開發環境，為AI智能體提供其運行所需的基礎設施上下文，從而讓AI能夠最佳化自身提示並提升輸出質量，同時通過沙箱環境確保安全。零售商沃爾瑪在冷藏單元中部署物聯網感測器，以遠端監控溫濕度，減少易腐商品的損耗。這些案例都表明，通過將基礎設施與業務目標進行深度繫結，企業能夠最大化技術投資的回報，並創造出獨特的競爭優勢。 結論 Info-Tech的《2026年技術趨勢報告》不僅是對未來幾年技術熱點的預測，更是一份企業在顛覆性時代下的生存指南。報告清晰地揭示了未來的核心矛盾：一個在政治和經濟上日益割裂的世界，與一個在技術上日益由自主智能連接的世界。 在這一背景下，被動的數位化轉型已遠遠不夠。企業必須主動擁抱一場深刻的結構性變革。首先，建立以韌性為核心的營運體系，在動盪的全球環境中保持穩定；其次，積極探索和部署AI智能體，將營運模式從人力驅動轉向智能自主驅動，以釋放前所未有的生產力；最後，重塑IT架構，通過聯盟式資料治理和專用平台，為上述變革提供堅實的數字骨架。 這場變革充滿了挑戰，從供應鏈重組的短期陣痛，到AI失控的長期風險，再到企業文化的適應與重塑。然而，正如報告標題所言，“顛覆加深”之處，亦是“機遇拓寬”之時。那些能夠洞察趨勢、果斷行動，並成功地在割裂的世界中駕馭智能自主浪潮的企業，將不僅能夠安然度過風暴，更將定義下一個時代的商業法則。 (歐米伽未來研究所2025) < Previous News Next News >

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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 【昇銳電子】產業AI落地實證與擴散會議 昇鋭電子股份有限公司(桃園市八德區長興路673號) 2021 1004 10:00~12:00 本會入選經濟部工業局110年AI智慧應用服務發展環境推動計畫-推動關鍵領域AI應用加值之產業AI計畫推動小組(SIG)，本會會員入選有昇銳電子(產業鏈類)及興創知能(先期產業類)等2家公司。本會已於2021年10月4日完成參訪昇鋭電子股份有限公司。 < 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 >

2022 臺灣企業資安曝險大調查 2022-09 KPMG 安侯建業 新聞來源： https://home.kpmg/tw/zh/home/insights/2022/09/2022-tw-cyber-risk-report.html 臺灣企業CEO 普遍對組織的資安有著高於全球平均的信心，為了避免企業「自我感覺良好」，協助臺灣企業找尋「盲斷層」突破盲點，KPMG 彙集資安各領域專家，發表2022 年臺灣企業資安曝險調查報告... 近期臺灣面臨地震活躍期的風險，而平時不顯露於地表的盲斷層又開始被民眾廣為討論。依據網路維基百科所述，盲斷層是指沒有破裂到地表，因此從地表看來沒有任何異狀的斷層類型。大部分在地圖上也沒有繪製出該盲斷層的實際位置，只有當發生突如其來的地震時才可能被人們所發現。而臺灣企業所面臨的資安風險，也有著相似的「盲斷層」現象。 KPMG 安侯建業透過CEO 2022 outlook 觀察到，臺灣企業CEO 普遍對組織的資安有著高於全球平均的信心，為了避免企業「自我感覺良好」，協助臺灣企業找尋「盲斷層」突破盲點，KPMG 彙集資安各領域專家，發表2022 年臺灣企業資安曝險調查報告，KPMG 資安曝險大調查針對六大產業，包括金融、半導體、電腦及周邊製造、電子商務、供應鏈核心及新創。透由報告發現台灣本土企業潛在資安風險，讓臺灣各產業能夠透過駭客的視角，全面性審視企業目前網路防禦現況是否充足、應變人力是否齊備。報告經抽樣調查60 家臺灣企業的平均曝險僅為C 級（70 ～ 80 分），通常具備一般技術的駭客就能入侵。 【2022臺灣企業資安曝險大調查】（即刻下載，掌握企業資安風險） https://assets.kpmg/content/dam/kpmg/tw/pdf/2022/09/2022-taiwan-cyber-risk-report.pdf 本調查主要發現： 1. 多數企業輕忽社群媒體所衍生的網路攻擊 大部分企業都擁有社群媒體的專頁，且員工也非常容易於社群媒體上暴露自己的公司聯絡資訊，導致駭客發動魚叉式精準社交工程時，成功得手機率大增。 2. 臺灣各產業資安人員能量均嚴重不足，企業資安人力亮警訊 臺灣企業在人力資源風險 (Human) 中，於「資安團隊戰力」相關成績顯示，資安人力缺口十分明顯。60 家受調企業中，經外部情資分析顯示，就可能有高達一半以上企業未配置 CISO或資安人員。 3. 供應鏈核心產業亟需加強網路防護 原物料、運輸等供應鏈核心產業，不僅在平均網路防護分數墊底，該產業更有高達近 50% 的企業落在整體排名的倒數 15名，網路防護亟待加強。 4. 金融業網路防護表現仍最佳，但面臨高度挑戰 金融業於各面向的平均分數皆取得優異的成績。但因金融網路犯罪利益巨大，讓金融業今日仍飽受內外部威脅與挑戰。 5. 導入並驗證資安國際標準，將顯著降低資安曝險 本調查發現取得國際資訊安全認證能顯著的提升資安能力，根據分析調查結果發現，在 60 家台灣企業中，其中有 21 家企業有取得國際資安管理標準認證。對比曝險分數可以發現，成績越高的群組，導入並驗證國際資安標準的比例越高。 < Previous News Next News >

成立宗旨 成立宗旨 理監事 顧問團隊 專業委員會 公會章程 成立宗旨 理監事 顧問團隊 專業委員會 公會章程 　　台灣智慧安防工業同業公會成立於 2019年12月，會員主要從事影像或語音數據之監控、感應、偵測、門禁、對講與防盜警報設備或系統等軟硬體研發、製造、加工及相關資料分析辨識之整合應用服務（含系統裝設與修護）。 公會成立之宗旨為協助安防產業發展，進行產業升級、新研發，提升產品與服務品質及應用範圍，將積極扮演政府部門交流、溝通協調的角色，努力為會員爭取更多權益。 　　台灣智慧安防工業同業公會致力打造一個為產業提供 集資訊交流、技術合作、供需對接、服務於一體的全方位資源平臺，通過高層對話、專案對接、展會論壇等形式促進產業鏈合作。 　　公會透過參與產品標準制訂、安防工程人才培訓等服務，推進行業規範永續發展；為產業開拓市場、促成技術與資本對接、資訊安全與智慧財產權保護等方面提供實質性幫助，攜手產業邁向高端發展。 姓名 手機 Email 請寫下您的建議 送出訊息 Thanks for submitting! 第二屆理監事名單 任期自111年12月16日起至114年12月15日止 江添貴 理事長 昇銳電子股份有限公司 董事長 李新榮 副理事長 杭特電子股份有限公司 總經理 理事長 & 副理事長 藍明振 常務理事 馥鴻科技股份有限公司 董事長 張隆進 理事 悅達科技股份有限公司 業務副總 張達明 理事 漢軍科技股份有限公司 副總經理 張嘉元 理事 慧友電子股份有限公司 資深經理 常務理事 & 理事 郭吉榮 理事 鎧鋒企業股份有限公司 董事長 陳子恆 理事 通航國際股份有限公司 總經理 黃義宏 理事 躍訊實業有限公司 總經理 連智民 候補 理事 維夫拉克股份有限公司 董事長 張心瑜 候補 理事 翔光工業股份有限公司 產品經理 Composition Cotton 95%, linen 5% Care instructions Machine wash Line dry Iron medium heat Avoid fabric softeners Only non-chlorine bleach when needed Get in Touch 123-456-7890 info@mysite.com

第二屆第12次理監事聯席【會議紀錄】 2025年8月21日 上午6:30:00

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 >

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