面對全球挑戰應有的堅韌與創新 2024-01-06 經濟日報 社論 新聞來源： https://money.udn.com/money/story/5628/7689380 揮別波折的2023，充滿挑戰的2024隨即而來！如何面對各方看法不一的台灣政經新局，是多數產業界關心的課題。尤其未來所面對的全球產經形勢更加地嚴峻，有挑戰也有大機會。 全球產經形勢包括：一，地緣政治衝突強度加大：美中對抗持續且強度更大，各種聯盟出現更增強對峙的態勢，也改變了產業及科技發展格局；烏俄及以哈戰爭持續，不僅影響東西方聯盟之間的角力，同時牽動能源供給及原物料價格。 二，超高齡社會的來臨：高齡人口數量和比例快速增加，加重扶養負擔；全球勞動力平均年齡上升，已開發國家勞動力短缺，各行業缺工問題浮現；鄉村人口外移，致使城市居住空間更加擁擠，舊式城市規劃不敷使用；少數金字塔頂端階層財富累積快速，掌握全球大多數的資產。 三，極端氣候影響：極端氣候造成各國許多生命與財產的損失，無以計數的人民流離失所。聯合國氣候變遷小組（IPCC）在名為「1.5°C的全球暖化」報告提出警告，全球必須致力守住1.5°C以下的氣溫提升為目標，否則可能引起不可逆轉的氣候變化，屆時將為全球產業與維生系統帶來全面災害，碳排淨零行動成為全球的共識，相關的政策與管制也不斷出籠。 四，數位科技帶來的創新與破壞：如人工智慧、大數據、混合實境、物聯網等，這些數位科技對於人類生活帶來全面、巨大影響，這也是為什麼聯合國開發計畫署指出：「數位科技所帶來的變革潛力如此之大，甚至可被稱為第四次工業革命」。不過，水能載舟亦能覆舟，數位科技的出現，也可能破壞既有的經濟系統、造成大量失業人口的問題，尤其是高度常規的、重複的工作型態，愈有可能被新興的科技所取代，而勞動被取代的結果，可能進一步鬆動既有的勞資關係、社會福利供給結構，進而影響到社會的各個層面。尤其數位科技更可能危害到個人隱私，產生各種倫理風險，必須未雨綢繆。 不過，以上的課題雖然棘手，但我們有信心台灣產業可以在既有的基礎上迎接挑戰。回顧以往台灣產業篳路藍縷所創造的成就，我們應該引以為傲。台灣地狹人稠，天然資源不足，但經過數十年的發展，台灣在政治、產業經濟與社會文化等方面，都有著令世人肯定與驚艷的成就。 台灣是「華人民主政治的先驅者」：雖然處於資源稀缺，災害頻仍的島國環境，但嚴峻的形勢化育台灣人民嚮往民主自由的性格，及樂於與世界為友的特色。 台灣是「人類數位文明的貢獻者」，資訊電子與半導體產業，融合歐、美、日各國的技術精華，進而以先進製造技術與完整的產業鏈而獨步全球；台灣是「全球科技資源的整合者」，中堅企業及隱形冠軍的影響力無遠弗屆，以代工形式在各產業國際大廠的均勢關係上，展現了高度的智慧。 台灣是「東亞與世界文化的大熔爐」，在南島語族的基礎上，承襲了中華（故宮、儒釋道、飲食、語文…）、西歐、美國與日本（建築、基建、農林、語文…）文化的洗禮，加以近年東南亞與香港新住民的持續融合，佐以印度大乘佛教與西方基督、天主教會的多元發展，形塑了台灣豐厚的生活型態與精神文明。 這些成就體現出台灣人民及產業在複雜多變的全球總體環境中不畏困難，勇敢克服挑戰的創新精神與堅強韌性。雖然未來的路途仍充滿荊棘與挑戰，但只要持續發揮創新和強固韌性，相信台灣產業可以化挑戰為契機，應對未來複雜多變的全球總體環境，拓展更多的市場商機。 < 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

第二屆第四次理監事聯席會議紀錄 2023年8月24日 上午6:00:00

第二屆第一次監事會紀錄 2022年12月16日 上午4:00:00

理監事會議 講座花絮 展會活動照片 會員服務與交流 友會互動與交流 展會活動照片 請點選相簿放大觀看 1/10 2025/08/27-29 Security Exhibition＋Conference 2025 2025澳洲國際安全博覽會 1/12 2025/08/14-16 Secutech Vietnam 越南國際安全及消防設備與應用展覽會 1/10 2025/05/07-09 Secutech 2025 台北國際安全科技應用博覽會 1/37 2024/04/24-26 Secutech 2024 台北國際安全科技應用博覽會 1/36 2023/04/26-28 Secutech 2023 台北國際安全科技應用博覽會 開幕典禮貴賓剪綵 開幕典禮邀請副總統親臨致詞，以及交通部、內政部建築研究所、行政院災害防救辦公室、內政部消防署等代表列席參與，同步邀請本會、台灣區電信工程工業同業公會、台灣消防器材工業同業公會、社團法人臺灣防災產業協會、中華軌道車輛工業發展協會等貴賓出席開幕典禮。 主辦單位-法蘭克福展覽公司台灣分公司 蔡琴琴總經理開幕致詞 本會與必維集團(香港商立德) 及行動檢測服務(股)公司，設立聯合服務攤位(535) 開幕典禮貴賓剪綵 開幕典禮邀請副總統親臨致詞，以及交通部、內政部建築研究所、行政院災害防救辦公室、內政部消防署等代表列席參與，同步邀請本會、台灣區電信工程工業同業公會、台灣消防器材工業同業公會、社團法人臺灣防災產業協會、中華軌道車輛工業發展協會等貴賓出席開幕典禮。 1/30 2022/04/27-29 Secutech 2022 台北國際安全科技應用博覽會

成立宗旨 理監事 顧問團隊 專業委員會 公會章程 公會顧問團隊 范國清 院長 許泰文 校長/教授 林永松 教授 謝君偉 教授 周俊廷 教授 劉祥泰 博士/院長 盧濟豐 技術長 趙緝熙 律師 高傳凱 博士 蘇清偉 副總經理/資安長 洪為璽 教授 周宗保 榮譽理事長 趙正宇 榮譽顧問 國立中央大學 資訊電機學院 國立台灣海洋大學/河海工程學系 國立台灣大學 資訊管理學系 國立陽明交通大學 AI學院/海洋大學 資訊工程系 國立台灣大學 電機工程學系 萬能科技大學 航空暨工程學院 昇銳電子/台灣大學電機所 網路計算暨安全實驗室 協合國際法律事務所 資策會資安科技研究所副所長 富邦金融控股股份有限公司 國立政治大學 資訊管理學系 台灣車聯網產業協會 第九屆.第十屆立法委員/交通委員會 顧問群介紹 國立中央大學 范國清 院長 學術專長：人工智慧、圖形識別、電腦視覺、影像處理及機器學習 佛光大學亞洲大學(借調)副校長 科技部智慧計算學門召集人 工業技術學院 技術資詢委員 國立海洋大學 許泰文 校長 學術專長：近岸水動力、波潮流預報、 海洋能源策略、風浪預報及海洋水下技術 科技部水下技術專案(潛艦國造)召集人 行政院能源及減碳辦公室委員暨離岸風電組副召集人 中華民國海洋與水下技術協會理事長 國立台灣大學 盧濟豐 昇銳電子技術長 學術專長：資訊安全、無線射頻、商業自動化及資料探勘(Data mining) 明新科技大學 工業工程與管理系兼任教師 經濟部智慧財產局 外部兼任專利審查委員 「影像監控系統」 資安標準暨測試準則 編審委員 「智慧巴士資通系統」 資安標準準則 編審委員 富邦金融控股股份有限公司 蘇清偉 副總經理 專長領域：資訊系統研發建置與推動、科技(網路)犯罪偵防工作、電腦鑑識與數位證據解析、資訊網路安全、創新警政科技應用 警政署資訊室主任兼任警政資訊分析團隊召集人 新北政府警察局保安警察大隊長、資訊室主任、 刑事局數位鑑識組組長。 富邦金控資安長，兼任台北富邦銀行資安長。 榮譽顧問 趙正宇 銘傳大學公共事務學系研究所碩士 桃園市第1屆市議員 第九屆.第十屆立法委員／交通委員會 國立台灣大學 林永松 教授 學術專長：感測網路、大數據分析、雲端運算、生物辨識、資訊安全及行動通訊網路最佳化 國際性資訊安全組織(ISC)資訊安全研究獎 指導教授 5G無線通訊網路資源管理與共享最佳化 計畫主持人 異質雲端運算資源整合與效率管理計畫主持人 國立陽明交通大學 謝君偉 教授 學術專長：影像處理、圖形識別、電腦視覺人工智慧及深度學習 深度學習於空拍影像之車輛、道路及環境分析技術開發計畫主持人 針對銀髮族之智慧離床偵測及降低跌倒風險之即時通報系統開發設計計畫主持人 智慧型多模態醫學影像整合分析之臨床電腦輔助診斷系統 共同計畫主持人 協合國際法律事務所 趙緝熙 律師 專長領域：跨國投資、併購、糾紛處理、智慧財產權、資本市場、一般公司法務 美國紐約州律師 私立中國文化大學法律系兼任助理教授 2009年, Asia Law, Leading Lawyer 2009年, KPMG 學苑年度最佳講師 國立政治大學 洪為璽 教授 學術專長：科技策略、電子商務、物聯網應用、資訊安全管理、文字探勘 政治大學資訊管理學系主任 政治大學商學院CINTES研究中心主任 中華民國資訊應用發展協會秘書長 社團法人台灣服務科學學會理事 中華民國資訊管理學會北區會員代表 國立台灣大學 周俊廷 副教授 學術專長：無線網路通訊協議、超高速個人無線區域網路、認知型智慧無線網路及AI深度學習 飛利浦北美研究實驗室無線通信網路部 資深研究員 動見科技創辦人/CEO(科技部價創計畫) 2019美國Las Vegas消費電子大展(CES) 創新獎-邊緣運算新世代車隊管理系統 萬能科技大學 劉祥泰 院長 學術專長：策略規劃、經營管理、績效評估、生產管理及 企業診斷 台灣作業研究學會秘書長 科技部管理學門二複審委員 科技部特殊優秀研究人才獎勵 財團法人資訊工業策進會 資安科技研究所 高傳凱 副所長 學術專長：資通訊安全領域之政策規劃、推動及新技術研發等，均有重大成果 資策會資安科技研究所副主任 2017 年制定第一份 IoT 資安標準「影像監控系統資安標準-網路攝影機」 2021 年將國際工控資安認證制度 ISASecure 導入台灣 2021年制定IoT資安標準 -門禁系統資安標準 台灣車載資通訊產業協會 周宗保 顧問 專長領域：車聯網相關設備產業與市場有豐沛的人脈與資源 早期派駐德英兩國30年餘，並移民英國 制定1.0版的車隊管理產業規範(僅Data數據) 台灣車載資通訊產業協會榮譽理事長

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 >