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2025年 國際安防展 參展補助開跑！ 越南展：8月14~16日 澳洲展：8月27~29日 越南．澳洲 本會已申請2025年經濟部國貿署推廣貿易業務補助(海外參展)，計畫籌組澳洲、越南國際展會參展團，即日起開放會員報名！ 展覽日 期：8月14~16日 展覽地點：越南．胡志明市 SECC會展中心 參展費用：新台幣 125,000 /1標攤 公會補助：新台幣 60,000 /1標攤 （已額滿！） Secutech Vietnam是越南最大的專業安防展。它還將與越南消防與安全、越南 SMAbuilding同期舉行。該展會將展示物聯網和人工智能 (AI) 實現的硬件、軟件和組件的集成；2024年邀請到15,569位參觀者、同時進行了超過450+ VIP買主媒合會。 〔越南消防與安全〕得到了地方當局、協會和私營部門的支持，並利用了他們的基礎設施項目和不斷增加的消防安全設備投資。 〔Secutech Intelligence Building Vietnam (SMABuilding)〕滿足越南對智能建築解決方案日益增長的需求，以提高安全性、能源效率和管理。智能家居、公寓樓、商業建築和智能工廠是四個重要應用領域。 欲了解更多展會資訊： https://secutechvietnam.tw.messefrankfurt.com/hochiminhcity/en.html secutech Vietnam 2025參展補助開跑！ .pdf 下載 PDF • 1.16MB 展覽日期：8月27~29日 展覽地點：澳洲．雪梨 ICC會展中心 參展費用：新台幣 210,000 /1標攤 2025年4月30日前報名享早鳥優惠， 新台幣 200,000 /1標攤 公會補助：新台幣 100,000 /1標攤 （已額滿！） 澳洲雪梨安防展覽及會議已舉辦38年，是連接澳洲安防產業最重要的貿易活動。 2024年展會共吸引超過9,000位安全專業人士前來參觀交流；其中包含當地重要的企業單位，包含：• Acetek Health & Aged Care • AGL Energy • Asx Operations • Australian Broadcasting Corporation • Australian Defence Force • Australian Federal Police • Brisbane Airport EXTEND YOUR REACH • Bunnings Group Limited • CBA • Dept of Communities & Justice • Dept of Defence • Dept of Home Affairs • Olympic & Paralympic Games Group • Queensland Police Service - Protective Services Group • Sydney Airport Corporation • Sydney Trains • Wesfarmers • Woolworths Group…等。 欲了解更多展會資訊： https://securityexpo.com.au/whats-on [更新]2025澳洲國際安全博覽會-Security Exhibition＋Conference_貿友展覽 .pdf 下載 PDF • 1.59MB ［安防公會］2025年澳洲國際安全博覽會－參展報名表 - 貿友展覽 .pdf 下載 PDF • 146KB 參加由公會籌組之參展團者，申請補助之條件： 1). 依經濟部合法登記設立之進出口公司或商號 2). 前一年具有進出口實績 3). 必須為本會會員 有意願參加之廠商，請洽詢： 台灣智慧安防工業同業公會 秘書處 02-22210617 / Email: tiaiss@tiaiss.org.tw 1/1 < Previous Project Next Project >

推動台美策略結盟 強化智慧製造系統 2022-07-26 經濟部工業局 新聞來源： https://www.moea.gov.tw/MNS/Populace/news/News.aspx?kind=1&menu_id=40&news_id=100967 經濟部台美產業合作推動辦公室、台灣智慧自動化與機器人協會與美國賓州匹茲堡機器人協會，簽署產業合作備忘錄，鏈結台美機器人與人工智慧產業生態系。 因應智慧科技及數位轉型趨勢，強化台美智慧製造合作，經濟部台美產業合作推動辦公室(TUSA)、台灣智慧自動化與機器人協會(TAIROA)與美國賓州匹茲堡機器人協會(PRN)於2021年10月簽署產業合作備忘錄(MOU)，雙方三個單位攜手合作，共同搭建平台，鏈結台美機器人與人工智慧產業生態系。 在此台美交流平台上，為促進雙方智慧製造系統與機器人聚落的鏈結，挖掘商機與互利雙贏，TUSA、PRN及AIT於7月26日共同舉辦「2022台美智慧製造系統與機器人高峰論壇」，特邀賓州有人工智慧搖籃之稱的卡內基梅隆大學(Carnegie Mellon University)電腦科學學院(School of Computer Science)分享該校人工智慧的發展、機器人聚落，包含機器視覺解決方案提供商卡內基機器人公司(Carnegie Robotics)及人形協作機器人Agility Robotics分享機器人產業技術與應用，洛克威爾自動化公司台灣區總經理譚世宏分享協助廠商進行數位轉型的實際智慧創新應用案例，工研院機械所饒達仁所長分享工研院與台灣機器人產業的聚落及發展。透過活動辦理與產業媒合，鼓勵業者結合台美雙方優勢，共同探討次世代的人工智慧系統整合與合作，形成一站式解決方案，快速切入市場需求。 經濟部工業局呂正華局長表示，過去臺灣已有良好的硬體製造基礎，現在受到美中貿易摩擦與COVID-19影響，牽動全球供應鏈重組，企業尤其是製造業將面臨智慧轉型的考驗。政府將掌握此契機，協助各產業導入AI、IoT、5G等智慧科技，搶占全球供應鏈的核心地位。經濟部工業局自2016年起推動「智慧機械產業推動方案」，也修訂「產業創新條例」，希望透過政府政策工具帶動產業智慧化升級轉型，朝向成為「亞洲高階製造中心」的目標邁進，並透過建立台美交流平台，鏈結雙方產業優勢，達到互惠互補的長遠合作夥伴關係。 在後疫情時代，零接觸、遠距工作及節能減碳觀念的推動下，各行業都在尋找合適的數位轉型機會，根據TrendForce (2021年8月) 研究顯示，2021年全球Smart Manufacturing Market規模為3,050億美元，預期在2025年可望到達4,500億美元，年複合成長率高達10.5%。 越來越多的台灣製造商正在向智慧工廠邁進，這反映了工業智慧化的全球趨勢。在台灣，政府已將智慧機械的開發作為其 5+2 產業創新的重要部分。智慧製造系統需要結合自動化技術、物聯網(IoT)和人工智慧(AI)，實現工廠營運及其管理的自動化，使用智慧機械改造工廠具有顯著降低營運成本並提高效率和品質的潛力。 台灣已經有很好的工具機、機器人零組件及精密機械的硬體技術基礎，美國則有強大的智慧系統整合能力、人工智慧演算法及機器人協作平台等技術。要趕上這波產業升級商機，應更強調產業聚落策略聯盟，藉由台美智慧製造產業交流與合作，互補雙贏，共逐全球商機。 < Previous News Next News >

< Back AIoT安防新視界 講座 公務人力發展學院福華國際文教會館2樓203教室(台北市大安區新生南路三段30號) 2021 0326 16:00~17:30 陳文昌榮譽理事長於2000年成立晶睿通訊，帶領晶睿以自創品牌 VIVOTEK 打進全球，進軍國際，在看準網路寬頻普及的趨勢下，率先投入網路攝影機 IPCAM市場，奠定晶睿在網路安全監控系統領導地位，陳榮譽理事長將分享、探討安防產業轉變，在接軌AIoT趨勢下，網路安防如何鏈結世界與驅動創新。 < Previous News Next News >

獨角獸盛世不再，AI 引領新創重返榮耀 2024-05-09 科技新報 新聞來源： https://infosecu.technews.tw/2024/05/09/ai-will-lead-startups-to-renewed-glory-as-the-unicorn-boom-fades/ 全球新創產業在 2023 年度過了沉悶的「重整期」，大批獨角獸陣亡後，投資人與創業者紛紛將目光轉向最火熱，也最有前景的一項技術上，那就是人工智慧。 如果你不知道 2023 年堪稱是「獨角獸版」的新冠疫情，看看這個數據。在這一年內，終止營運的新創企業其募資金額總計超過 41 億美元，相當於 2019～2022 年 4 年的總和。估值超過 40 億美元的醫療保險新創 Olive、估值 38 億美元的智慧卡車車隊新創 Convoy 和估值 23 億美元的減塑紡織新創 Zume，全都在這一年關門大吉。 此外還有超過 20 家獨角獸，已經有 2 年沒有進行新一輪募資，其中包含台灣人也很熟悉的 Notion、AirTable 和 Grammarly。在一片低氣壓下，人工智慧，特別是生成式人工智慧（Generative AI）幾乎成為新創產業夜空中最亮的星。 根據 CBInsights 統計，從 2023 年第二季以來，只要是有導入 AI 相關技術的新創，獲得資金便至少多出 20%，如果進行到 B 輪以上的大型案子，AI 相關新創獲得投資的金額甚至會多出 59%。Startup Genome 新創生態系報告則指出，2023 年 AI 和大數據是最受投資人關注的產業，占據全球新創投資市場總金額 28%。巧合的是，2023 年美國消費性電子展（CES）上，台灣參展的 96 支新創團隊中，占比最高的領域就是 AI 與機器人（28%），而與 AI 密不可分的數位醫療、智慧城市與環境永續也分別占比 20% 與 18%。 這一切資訊都指向一個明確的趨勢，AI 已成為貫串所有產業的核心技術，Microsoft 不僅重金投資 Open AI 100 億美元，同時也在產品、人力與資料端全力支持，就是想要穩定領先基礎。Amazon 和 Google 也不落後，接連端出多種 AI 應用服務，最讓業界震驚的莫過於蘋果公司，他們狠心終止開發十年的電動車專案，目標就是要整合資源，全力開發生成式 AI 項目。 雖然 AI 無疑是 2024 年最重要的新創趨勢，但除了手握巨量資源的科技巨頭外，新創公司的焦點還是落在如何具體應用到不同領域，TrendForce 分析師認為，2024 年值得特別關注的有三大領域，分別是資安、智慧醫療和零售服務。 AI 技術飛躍，成資安雙面刃 在資安戰場上，AI 已經成為最強的矛和盾，入侵者利用 AI 降低攻擊難度，防守方則使用 AI 偵查弱點，不過在產業現況來說並沒這麼單純。由於資安攻擊防不勝防，多數企業又抱持著消極心態，「目前在業界來說，AI 對資安最大的幫助並非未雨綢繆，而是亡羊補牢。」TrendForce 分析師曾伯楷指出，透過 AI 工具，資訊管理人員可以在資安事件發生後，快速的分析攻擊路徑，將漏洞補上，降低再次被攻擊的風險。 另一方面，由於資安人才缺乏，許多資訊管理人員都是兼職做資安，在生成式 AI 技術導入後，包括 Cisco、Palo Alto Network 等，都利用 GAI 簡化操作難度，管理人員可以用自然語言對談的方式，就完成過去相對複雜的資安工作。 儘管如此，對多數中小企業和終端用戶來說，佈署完整而強力的資安防護成本仍然高昂，成果卻難以體現。為了進一步提高資安防護力，許多製造商開始轉向從供應鏈上游展開資安佈局。以台灣最重要的半導體產業為例，就催生出硬體資安新創 Jmem Tek，他們改變傳統晶片寫入方式，藉由熔絲與反熔絲編程法將傳統的一位元進階成多位元，並且打亂位元排列組合、避免駭客進行反向工程，能夠應用在 IoT、車用電子和電子硬體防護等產業。目前許多晶片大廠，像是英飛凌、ARM 和 NXP 都在上游開始做硬體防護，讓這個市場快速成長。 隨著網路攻擊、資訊外漏等事件不斷增加，提供資安新創迎來巨大的成長空間。根據 Global Information 估計，光是物聯網安全市場規模，到 2024 年將達到 66 億美元，預計 2029 年將達到 280.1 億美元，2024~2029 年複合年成長率為 33.53%。IDC 也預估，到了 2026 年全球將有 30% 大型企業將會藉由投資自主安全營運相關的方案，提高資安事件的修復、管理和應對效率。但分析師也提醒，資安是高度敏感議題，一般來說企業還是會偏好傳統大廠，新創公司要切入的難度不低，是這個市場的風險，也可能是機會。 全球大廠率先投入 AI 打造精準醫療，台灣新創跟緊腳步 醫療與藥物是智慧化投入程度最高的領域之一，特別在藥物研發部分，過去十年來，美國食品藥物管理局核准的藥物中，有三分之二都屬於「小分子」藥物，而小分子藥物研究高度依賴 AI 協助，領導廠商包括 Recursion、Benevolent 以及最受關注的 Insilico Medicine，他們在 2023 年宣佈，史上第一款從頭到尾由 AI 研發的抗癌藥物 ISM5411，已經進入第二期臨床試驗，讓世人看見 AI 開發藥物的速度有多麼驚人。 如果你覺得這些廠商名不見經傳，影響不大，那就錯了。Insilico Medicine 的主要投資人就是強生集團的子公司 Janssen 製藥；Recursion 的主要投資者則是拜耳子公司 leaps，羅氏藥廠更是與多個 AI 藥物開發新創合作，能夠精準找出受試者，加速開發過程。 另一個更像是科幻電影情節的智慧醫療，也在 AI 助陣下逐漸實現，就是腦機介面（brain computer interface），在四肢癱瘓患者的腦部植入微小的處理器，就能協助患者用意識控制手機和電腦滑鼠，目前已經有 2 家新創公司開始人體試驗，分別是馬斯克主導的 Neuralink，和貝佐斯與比爾蓋茲投資的 Synchron。 根據 TrendForce 預估，全球智慧醫療市場有望於 2025 年突破 3,600 億美元，而台灣的數位醫療相關營業額在 2022 年也已經達到 500 億新台幣，隨著 AI 進步，台灣智慧醫療相關新創也成為一大焦點，有 14% 創業者都投入這個領域。 為消費者量身打造，智慧零售帶動大量商機 人工智慧一直都被期望能夠改變零售樣貌，但在隱私問題關注增加後，一度發展受挫，曾經紅極一時的無人商店，也因為種種因素發展停滯，不過生成式 AI 卻可能帶來新的轉變。 「零售科技現在進一步朝向高度客製化方向移動，有點像是每個消費者的購物顧問。」TrendForce 分析師曾伯楷表示，以全球零售龍頭 Walmart 為例，他們提供的最新服務就是基於生成式 AI，消費者只需提出一個模糊的需求，電腦就能提供完整的一套購物清單，再讓消費者決定要買哪些東西。 舉例來說，目前我們在網路購物時，已經很習慣搜尋我們想要的產品，但常常要花去很多時間，還很容易分心逛到別的東西去。「現在消費者可以告訴購物助理，我想要幫兒子辦一個烤肉派對，電腦就會幫你準備好所有你需要的物品。」 Walmart 的這項服務是由 Microsoft 提供，背後的技術來自 Open AI，而 Google 則已經準備將生成式 AI 帶入企業服務專員，在 B2B 領域提供服務。網購巨頭 Amazon 也已經開始測試購物助理 Rufus AI，準備改變零售樣貌。 除了大型企業，台灣也有一些新創借力人工智慧，在不同領域推動智慧零售，咖米科技就針對台灣人很愛買的保健食品，推出一站式客製化服務，讓消費者可以量身定製自己需要的保健品，而不用被瓶瓶罐罐堆滿家中，試圖在這個垂直領域搶占商機。 AI 無所不在 除了上述領域能夠看到結合 AI 的新創快速發展，2024 年的新創產業裡，人工智慧可說是無所不在，無論是全球市場或是台灣本地，這個趨勢都相當明顯。不過 AI 並非萬靈丹，就以最熱門的 ChatGPT 為例，許多用戶都開始反應，電腦給出的回應品質下降，這一方面可能是使用者的「期望值」提高了，一方面也是因為運算資源有限，平台可能會減少機器學習系統內的參數量，來節省時間和算力。 最後也最重要的，無論在哪一個領域導入 AI，都必須考量到出錯的可能，因此核心決策，最終還是要交給人類處理，才能避免犯下不可挽回的錯誤。 < Previous News Next News >

電動車充電樁通訊協定漏洞，導致遠端關機、資料與電力遭竊 2023-02-06 TWCERT/CC 新聞來源： https://www.twcert.org.tw/tw/cp-104-6916-edc93-1.html 以色列資安廠商 SaiFlow 近日發表研究報告，指出該公司發現多種電動車充電系統的舊版通訊協定，內含兩個資安漏洞，可能導致駭侵者遠端關閉充電樁，甚至用以竊取資料與電力。 被發現存有漏洞的電動車充電通訊協定為 Open Charge Point Protocol (OCPP) 1.6J 版本；該通訊協定使用 WebSocket ，讓電動車充電樁與管理系統 (Charging Station Management System, CSMS) 服務提供者進行溝通。 SaiFlow 指出，OCPP 標準並未明確定義在已啟用與充電樁連線的情形下，CSMS 應如何接受來自充電樁的新連線要求；這種對於多個已啟用連線操作方式缺乏明確定義的情形，導致駭侵者有機會藉由充電樁和 CSMS 間的通訊連線來下手發動攻擊。 SaiFlow 說，攻擊者可以冒充為一個已經建立連線的充電樁，對 CSMS 發動兩種攻擊：在 CSMS 供應商關閉原有連線並建立新連線時，對 CSMS 發動服務阻斷攻擊 (Denial of Service, DoS)、以及侵入並攔截 CSMS 與已連線充電樁之間的連線，以竊取充電中車主的各種個資，包括駕照資訊、信用卡號、CSMS 登入資訊等。 SaiFlow 也指出，新版的 OCPP 2.0.1 已經修補好這兩個漏洞，其做法為當單一充電樁同時進行多個連線時，進行更頻繁的充電樁登入資訊提供要求。 隨著電動車的快速普及，其相關基礎設施的資安也將更為重要。建議公用充電樁業者應加強充電樁暨管理系統的資安防護與軟硬體升級作業，以免成為駭侵攻擊的受害者。 < Previous News Next News >

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 >

《亞洲詐騙調查報告 – 台灣篇》近四成民眾收過疑似 AI 詐騙簡訊 2024-10-16 説資安新聞網 新聞來源： https://cybersecurenews.com.tw/expert-talk-049/ 隨著民眾接觸資訊的方式日益多元，根據調查報告分析，三大詐騙管道為電話（59.9%）、社群平台 (54.5%)、簡訊 (52.5%)，有超過五成民眾都曾在上述管道接觸過詐騙。 第二屆 GASA 亞洲防詐高峰會 於今年 10 月 21 日至 22 日在新加坡舉辦，擔任基石成員的信任科技服務商 Gogolook（走著瞧股份有限公司）旗下數位防詐 APP Whoscall 與全球防詐聯盟 GASA（Global Anti-Scam Alliance）以及 ScamAdviser 發布《亞洲詐騙調查報告 – 台灣篇》，深度解析台灣詐騙面貌，據報告指出台灣有超過五成民眾每週都會接觸到詐騙，更有近三成的受騙者在與詐騙接觸後的一小時內支付金錢或提供個資。 詐騙手法 Top 10 : 個資盜用與購物詐騙，名列前二名 GASA《亞洲詐騙調查報告》針對亞洲多達 13 個地區、將近 2 萬 5,000 名受訪者進行調查，列出亞洲各國最常見的十大詐騙手法，整體而言「個資盜用」、「投資詐騙」與「購物詐騙」為亞洲所有國家共同的隱憂，而台灣民眾遭遇最多的威脅為個資盜用（24%），這與日本、新加坡、越南、中國等六個國家的情況相似，顯示個人資料安全問題日益嚴峻，而在韓國、馬來西亞、巴基斯坦與印尼則以投資詐騙最猖獗。 台灣詐騙手法其次是購物詐騙（15%）和企業冒名詐騙（13%），持續威脅消費者的財產安全。假冒親友詐騙與中獎、優惠詐騙則同以 11% 並列第四，反映詐騙手法多樣化且靈活運用人際信任及促銷心理。其他常見的詐騙手法還包括投資詐騙、假帳單詐騙及慈善捐款詐騙，突顯詐騙防範已成為現代社會不可忽視的重要課題。 社群詐騙興起 ! 電話與簡訊仍為主流手法 隨著民眾接觸資訊的方式日益多元，詐騙集團的手法也逐漸滲透至日常資訊中。根據 調查報告分析，三大詐騙管道為電話（59.9%）、社群平台（54.5%）、簡訊（52.5%），有超過五成民眾都曾在上述管道接觸過詐騙。台灣數位信任協會 9 月發布的《 冒名詐騙報告 》也指出，去年至今年 4 月，冒名類型的高風險電話和簡訊高達近 300 萬筆，詐騙的話術多元、手法不斷翻新，商品促銷、繳費逾期、急難捐款都是常見的手法。這也強調了防詐工具的重要性，例如 Whoscall 這類即時防詐辨識服務，能協助民眾迅速識別可疑來電、簡訊、網站，並在短時間內作出判斷，有效降低受騙風險。 值得注意的是，社群平台詐騙的成長速度最快，與去年相比增加了 21%，並躍升為第二大詐騙管道。其他常見的詐騙媒介還包含通訊軟體、Email、電商平台及交友軟體等，顯示詐騙逐漸轉移至數位平台行騙。然而，仍有超過兩成的民眾表示曾收過詐騙包裹或信件，說明實體詐騙與線上詐騙依然並存。 Facebook 假投資與購物詐騙猖獗 根據《Digital 2024: Taiwan》報告顯示，台灣約有 1,920 萬名社群用戶，相當於八成 民眾皆有使用社群的習慣，其中，最常使用的社群平台依序為 LINE、Facebook 與 Instagram。GASA & Whoscall《亞洲詐騙調查報告 – 台灣篇》指出，詐騙集團專門鎖定用戶數最多的社群平台行騙，當台灣民眾在被詢問到曾經在哪些數位平台遇過詐騙時， Facebook 連續兩年名列詐騙接觸率最高的社群媒介，有超過六成（63 %）的民眾都曾遇過詐騙。詐騙手法以假冒投資專家或分析師投放廣告為主，引誘受害者投資或購買假的金融產品。此外，亦有詐騙抓緊民眾貪小便宜的心態，在 Marketplace 以低於市價的商品吸引消費者，當民眾匯款後卻未收到正品，或是根本未收到商品。 LINE 作為台灣使用率最高的通訊軟體，詐騙行為也層出不窮。調查顯示，近五成（47.1%）的用戶表示曾在 LINE 上遇到詐騙。除了常見的假投資群組外，詐騙手法也越來越多樣化，利用「寵物投票」、「輔助認證」等誘餌，吸引民眾點擊不明連結，引導至釣魚網頁輸入 LINE 帳號密碼或個資，進而導致個資或 LINE 帳號密碼被盜用。 除了 Facebook 和 LINE，Gmail、Instagram 和蝦皮購物（Shopee）也是詐騙集團活躍的場域。約 25% 的台灣民眾表示曾在這些平台上遭遇詐騙，透過釣魚郵件、假冒賣家，或是透過社群媒體進行詐騙。隨著社群媒體與數位平台的普及，詐騙的手法與管道也變得更加多元且隱蔽，民眾需時刻保持警惕，避免落入詐騙陷阱。 生成式 AI 蓬勃發展！AI 成為詐騙集團犯罪新利器 隨著 AI 技術日益成熟，不僅被用來加速工作流程，也被詐騙集團作為犯罪工具。本次《亞洲詐騙調查報告 – 台灣篇》調查了民眾是否曾接觸過疑似運用 AI 的詐騙，結果顯示，近四成的受訪者自認曾收到疑似透過 AI 生成的詐騙簡訊。詐騙集團利用生成式 AI 大幅加快詐騙文本生成速度，再加上 AI 機器人自動化發送，使詐騙案件層出不窮。 除此之外，詐騙手法還蔓延至即時通訊軟體、語音電話、圖片與影片等多種形式。尤其是「深偽技術」（Deepfake）的崛起，透過變臉或聲音合成冒充親友或名人進行詐騙，風險不斷增加。特別是經常在電視或公開場合發言的公眾人物，更容易成為詐騙集團的目標。只要截取其發言片段，AI 就能模仿出與真人極為相似的聲音，讓 AI 詐騙案件更防不勝防。 完整 Whoscall《亞洲詐騙調查報告 – 台灣篇》下載： https://www.gasa.org/research 關於 全球防詐聯盟 GASA 全球防詐聯盟 GASA（The Global Anti-Scam Alliance）致力於保護全球消費者免於詐騙威脅，透過教育提高對消費者對詐騙風險的意識，並推廣實用的防詐工具、促進各界防詐交流、投入詐騙研究分析，分享最新的的防詐趨勢觀點。 相關新聞：國家級網路攻擊 威脅台灣／工商時報 https://www.ctee.com.tw/news/20241023700166-439901 < Previous News Next News >

< Back 第一次門禁系統資安標準制定草案工作小組會議 集思台大會議中心拉斐爾廳 (台北市羅斯福路四段85號B1) 2021 0820 14:00~16:30 門禁系統資安標準內容討論及產業意見蒐集 < Previous News Next News >

臺灣生成式AI產業可聚焦四大垂直領域 2023-11-29 工商時報 新聞來源： https://www.ctee.com.tw/news/20231129700018-431307 最值得臺灣投入生成式AI的產業應用，在「內容創作」上，可透過個人分身結合臺灣手語生成與辨識，建立智慧化手語轉譯服務，幫助聽障者融入社會。圖／工商時報 GAI浪潮來襲，2023被視為生成式AI元年，臺灣已有許多產業無不積極導入相關技術噢應用，希望提升產業競爭力與效率。剖析目前生成式AI的生態系版圖，主要是由晶片、伺服器、軟體平台、以及垂直應用等項目組成，臺灣想要在AI競賽彎道超車，可聚焦在「垂直應用」領域多加著墨，搭配產業GAI化、GAI產業化兩大策略，也就是選擇具臺灣優勢的垂直領域深入耕耘，進而帶動資服軟體商機與新創機會，加速釋放生成式AI潛力，成功協助產業升級轉型。 生成式AI在2022年底爆紅，工研院早已擘劃「2035技術策略與藍圖」，聚焦「智慧生活」、「健康樂活」、「永續環境」、「韌性社會」四大應用領域方向，加上院內累積30多年自然語言與電腦視覺技術能量，如今已擁有數百件人工智慧專利智財的基礎。盤點四大領域相關技術與能量，最值得臺灣投入生成式AI的產業應用，分別是在「研發製造」、「營運及供應鏈」、「商務服務」、「內容創作」四項領域上，可以分別舉例說明。 由於臺灣製造業佔比極大，企業特別關注的是製造研發能量、供應鏈韌性這兩項指標。在「研發製造」領域中，由於對成品的正確性要求較高，建議可從具豐富領域資料的應用來切入較有優勢；而在「營運及供應鏈」領域中，則可先從企業內部應用著手，例如開發行政文件擬稿、教育訓練輔助為題目，再逐步擴展到供應鏈。 觀察業界目前使用生成式AI的具體情境，目前製造研發所遇到的難題，主要來自於人才的缺乏，包括數位IC工程師人才缺乏、工業編程人員能力難養成，特別是在撰寫暫存器傳輸級（register-transfer level；RTL）的程式碼專業領域上，需要長期累積豐經驗才能勝任。若能透過生成式AI的協助，未來可望提升晶片RTL Code和工業電腦輔助製造（Computer-Aided Manufacturing；CAM）G代碼（G-code）的設計效率，再匯集有經驗的工程師加以回饋來訓練AI模型，進而在工程師開發RTL Code或·G-code的過程給於適當提示，補足人才與經驗短缺等問題，提升工作效率。 而在企業營運這部分，過去多半仰賴人工撰寫企業文件，複雜報告或大量文件衍生耗時長挑戰，也容易出現拼寫或語法錯誤，或專業領域文書經驗傳承不易等困境。目前已有許多企業導入生成式AI進行行政文件撰擬與行政流程上，根據統計，確實可提升25％企業文件撰寫效率，亦可節省時間、提高文件品質，未來更能建立組織內部的專業知識庫，達到事半功倍的效益。 不僅如此，我們也在「商務服務」領域中觀察到越來越多創新的商業模式，例如零售、電商業者過去3D人工建模至少要3～7個工作天，未來藉由拍照掃描、口語下指令方式，透過AI生成商品銷售模型與場景產製，讓3D物件製作期直接縮短到30分鐘；專業3D建模軟體亦可搭配生成式設計輔助功能，簡化藝術家、設計師工作流程，同時讓消費者享受3D購物體驗，在數位世界掌握產品規格與細節。 最後，特別值得一提的是在「內容創作」部分，更可善用生成式AI落實社會公益。舉例來說，目前專業手語老師極度缺乏，平均100人只有一位手語老師，導致聽障者在業務申辦或社會溝通仍遭遇許多困難。未來若能透過個人分身結合臺灣手語生成與辨識，建立自動化、智慧化手語轉譯服務，打造專門的手語播報節目，幫助聽障者融入社會，在生活上的溝通也能更為便捷。 從上述四項AI應用實例可以發現，善用生成式AI系統或服務，確實能發揮不少效益；不過更重要的是，由於相關技術進展快速，如何持續投入累積技術能量，進行應用評估與導入，仍須仰賴產官學研及各產業的大家協同合作，讓更多產業與民眾享受AI帶來的幫助與效益。 < Previous News Next News >

門禁系統如何踏出資安標準合規的第一步？ 2024-03-27 全球安防科技網 新聞來源： https://www.asmag.com.tw/showpost/12881.aspx? 繼影像監控系統（IP Camera、NVR/DVR、NAS）之後，行動應用資安聯盟又於1月30日正式發佈了「門禁系統資安標準V2.0 暨測試規範V2.0」，對門禁廠商來說，如何順利合規不啻為一大挑戰。 a&s媒體特別攜手法國必維國際檢驗集團（BUREAU VERITAS） 資安認證團隊規劃系列報導，將分別針對門禁管理平台、閘道控制器、讀取器、門鎖等資安標準，從第三方驗證公司的角度與廠商分享應關注的重點以及可落實的合規作法。敬請期待！ （圖片來源：123RF） 隨著科技的不斷進步，更多具備IoT功能的設備、系統和服務， 包括各項OT通訊協定（如ModBus、BACnet）、IT協定（如HTTPS、SFTP）以及各式各樣的傳輸技術，都已大量導入機電控制、冷凍空調、防火防災、防盜保全等智慧建築（Smart Building）領域。 面臨門禁系統安全挑戰， 全球資安標準趨勢 在這樣的大環境下，門禁系統不再僅僅是物理安全的象徵，其涉及的網路和資訊安全 （Cybersecurity）也愈發重要。卡巴斯基（Kaspersky Lab）的研究指出，2019上半年用於控制智慧建築自動化系統的電腦，就有37%受到惡意攻擊的影響;而關於人員門禁個資外流、大樓指示牌與顯示螢幕被駭、影像 監控系統影片流出⋯等相關新聞報導亦層出不窮，顯見物聯網資安的重要性。 從2020年開始，我們可以看到世界各國公佈了許多網路安全相關法規，從美國加州物聯網條例草案、日本的電信商業法、德國IT安全法案、歐盟的網路韌性法⋯等。毫無疑問的，為了因應各國市場的法規和監管機構，智慧建築生態系的一線廠商們開始導入相關網路安全標準到自家的產品、系統，甚至是產品開發流程當中，例如ISA/IEC 62443、ETSI EN 303 645等國際資安標準， 皆是廠商可以採用的內容。尤其是ISA/ IEC 62443標準特別關注OT環境的安全風險，廠商可從公司治理、安全供應鏈、產品開發、產品生命週期四大層面塑造自家產品網路安全的基礎防護能力。透過遵循這些國際標準中的要求， 廠商不僅能夠保護其產品網路安全，還能樹立商業上的技術護城河。 台灣門禁系統資安標準3大基本測試項目 以台灣市場來說，政府相關單位於2021年即已著手門禁系統資安標準的制定，行動應用資安聯盟於今（2024） 年1月30日又發佈了「門禁系統資安標準V2.0暨測試規範V2.0」第1∼6部， 涵蓋了「門禁管理平台」、「門禁閘道控制器」、「門禁讀取器」、「智慧門鎖」、「人臉辨識門禁裝置」5項產品。然而新的規範上路也必然遇到許多挑戰，法國必維國際檢驗集團建議門禁系統廠商，要跨出合規的第一步，應從門禁系統安全規範中最重要的3個測試項目為主： • 門禁系統安全－第1-1部：一般要求事項中的5.2.2.3網路服務最小化測試。 • 5.2.3.2測試作業系統與網路服務是否存在CVSS v3評分為9.0分以上之常見安全脆弱性。 • 5.2.3.3網頁管理介面高風險等級脆弱性測試。 藉由這3項基本測試，門禁系統廠商們可以得知自家產品的現況，並思考團隊可以如何提升門禁系統產品的網路安全性。 測試1 ： 網路服務最小化 首先，我們先來探討門禁系統資安標準第一部5.2.2.3網路服務最小化測試。這個測試的目的是驗證門禁產品是否存在預期以外的網路埠，意即廠商在送測前須先了解其產品對外開啟的服務有哪些？是不是可從其他台主機可以被偵測的到？再來開啟這些服務是否合理？因為有心人士可透過不同服務的偵測與枚舉，進一步收集資訊找到攻擊點。因此藉由這項測試，我們可以確保門禁系統僅開啟必要且已知的網路服務，減少攻擊面、提高系統的安全性。這項檢測也有助於避免不必要的網路連接，減少潛在的風險。 測試2 ： 常見資安漏洞檢測 其次，5.2.3.2測試作業系統與網路服務是依照目前市面上已知的作業系統弱點下去做檢測，而規範以CVSS v3 9.0 為分界點。根據CVSS v3評分，如果存在9.0分以上的常見資安漏洞，系統將面臨極大的風險。因為這些已知的漏洞可能被駭客利用，導致系統遭受攻擊、資料外洩、或系統功能受損等危害。所以此檢測目的在於，要求廠商時時更新與修補門禁系統所在之作業系統，以避免未修補的漏洞成為潛在的攻擊入口。但由於資安事件層出不窮，即使通過測試，必維也還是建議廠商定期的做作業系統弱點檢測，並且時時做更新與修補，才能確保門禁系統的堅固性。 測試3 ： 網頁管理介面漏洞檢測 最後，5.2.3.3網頁管理介面高風險等級漏洞測試，目的是驗證門禁產品的網頁管理介面是否存在OWASP Web Top 10高風險等級的漏洞。OWASP （Open Web Application Security Project）是一個全球性非營利組織，而 Web Top 10是由其中的一群資安專家長期關注網路應用程式風險，並按照其嚴重性和頻率進行排名，旨在揭示最常見的10種網路應用程式安全漏洞。網頁管理介面通常是攻擊者入侵的一個重要入口，而在門禁系統的網頁管理介面中， 可能存在SQL注入攻擊、跨站腳本攻擊（XSS）、跨站請求偽造（CSRF）等高風險等級漏洞。此測試的目的就是確保這些漏洞不會成為潛在的攻擊入口，而常態性進行這類測試有助於及早發現並修補潛在的漏洞，提高網頁管理介面的安全性，防範潛在的攻擊。 結語 綜合而言，門禁系統的網路安全測試是確保整體系統安全性不可或缺的一環。先藉由網路服務最小化、常見資安漏洞以及網頁管理介面漏洞3項基本測試，可以讓廠商先了解目前產品的資安狀況，進而研擬修補或升級的計畫。隨著整體資安意識的成熟，我們能夠有效地強化門禁系統的網路安全性，保障用戶和資訊的安全。 < Previous News Next News >

中國研究干擾低軌衛星通訊：需 2,000 架無人機切斷台灣規模網路區域 2025-12-05 資安人科技網 新聞來源： https://www.informationsecurity.com.tw/article/article_detail.aspx?aid=12517&mod=1 這項研究顯示，中國正積極研究如何在未來可能的亞洲衝突中，切斷對手的衛星通訊能力。 中國兩所主要大學的研究人員在最新學術論文中指出，若要干擾星鏈(Starlink)等低軌衛星網路對「台灣規模區域」的通訊服務，需要動用 1000 至 2000 架無人機進行訊號干擾。這項研究顯示，中國正積極研究如何在未來可能的亞洲衝突中，切斷對手的衛星通訊能力。 烏克蘭戰爭經驗引發中國關注 自俄羅斯入侵烏克蘭近四年以來，衛星星座網路一直是烏克蘭軍方的生命線，即使面對持續攻擊，仍能維持網路與軍事通訊暢通。這項經驗引起中國高度關注。根據香港《南華早報》(South China Morning Post)報導，中國研究人員在 11 月 5 日發表於《系統工程與電子技術》的論文中，詳細探討了干擾大型衛星下行通訊的策略。 瑞士蘇黎世聯邦理工學院(ETH Zürich)安全研究中心資深網路防禦研究員 Clémence Poirier 表示， 各國政府和太空企業應將此研究視為重要信號，若中國與台灣之間發生衝突，干擾衛星連線將會是首要戰術。 低軌衛星具高度韌性但非無懈可擊 星鏈目前在低軌(LEO)運行約 9000 顆衛星，低軌網路難以干擾的原因，在於其衛星數量龐大、移動速度快，並採用多種技術來避免和修正訊號干擾。然而，中國研究指出，雖然困難且成本高昂，但並非不可能達成。 華盛頓智庫「戰略與國際研究中心(CSIS)」航太安全專案副主任 Clayton Swope 解釋， 鎖定衛星的網路攻擊之所以受到青睞，是因為它們造成附帶損害的風險較低，且較不容易升級緊張局勢。 他指出:「雖然實體摧毀攻擊仍是威脅，但在和平時期或緊張對峙階段不太可能發生，因為風險過高，容易引發全面衝突。相較之下，網路攻擊以及訊號干擾經常發生，屬於灰色地帶戰術，似乎較不容易意外升級威脅。」 台灣已部署備援衛星通訊方案 面對潛在威脅，台灣已採取預防措施。台灣已與 Eutelsat OneWeb 簽訂合約，這是另一個擁有超過 600 顆衛星的衛星網路，將在災難事件中提供連線服務。這項備援機制顯示台灣對衛星通訊安全的重視。 航太公司(The Aerospace Corporation)太空政策與戰略中心策略與國家安全主任 Sam Wilson 表示，由於美國等國轉向採用大型分散式衛星網路，反衛星飛彈(ASAT)等傳統武器的戰略價值已經降低。「擊落單顆衛星雖會造成損害並可能引發衝突升級，但無法讓整個網路失效。因此攻擊者開始尋求電子戰和網路攻擊等替代方式。」 太空已成軍事行動骨幹 Clémence Poirier 強調，太空已成為所有軍事行動的骨幹，今日沒有任何衝突不在某種程度上依賴太空。她指出，「衛星成為有價值的目標。」中國已制定了詳細的反太空作戰策略。解放軍在 2021 年提出的多域精準作戰概念，即整合陸海空、太空與網路的跨域作戰能力。然而，每個主要國家都在為太空領域的戰爭做準備，特別是在低軌衛星。 Poirier 建議，太空公司必須密切監控其系統，在民用和軍用客戶之間隔離網路，並在衝突發生時更新其威脅模型。這項研究提醒各界，衛星通訊安全已不再只是技術問題，更是國家安全的關鍵議題。 < Previous News Next News >

旅館房卡系統有漏洞，駭客可自製萬能房卡 2024-03-25 iThome 新聞來源： https://www.ithome.com.tw/news/161930 Dormakaba超過300萬個RFID電子鎖含有安全漏洞，允許駭客利用一對偽造的鑰匙卡解鎖單一飯店所有房間，即使修補工作從去年11月開始進行，但涉及複雜的軟硬體更新，截至今年3月只完成36%進度。 資安研究人員近日揭露，知名安全業者dormakaba所推出的RFID電子鎖Saflok含有一系列的安全漏洞，允許駭客利用一對偽造的鑰匙卡解鎖單一飯店的所有房間，並將相關漏洞命名為Unsaflok。此一型號的電子鎖經常被應用在飯店與住宅大樓中，估計有超過300萬個散布在131個國家的電子鎖受到影響。 dormakaba在1862年創立於瑞士，為當地知名且歷史悠久的安全業者，最初只是一家鎖匠與收銀機工廠，現階段所提供的產品已涵蓋實體門的五金、電子存取、入口系統、機械鑰匙系統、保險箱鎖，以及鑰匙系統等。 Unsaflok漏洞影響Saflok系統的電子鎖產品，包括Saflok MT、Quantum系列、RT系列、Saffire系列和Confidant系列等，涉及System 6000、Ambiance與Community等管理軟體，研究人員是在2022年發現相關漏洞，同年9月通知dormakaba，雖然dormakaba已開發並公布緩解措施，並自去年11月開始升級或更新飯店所使用的系統，不過截至今年3月，受到影響的門鎖中，只有36%已更新或更換。 根據dormakaba的說法，相關漏洞與用來產生MIFARE Classic金鑰的金鑰衍生演算法，以及用來保護底層卡片資料的第二次層加密演算法有關。 而研究人員則說，駭客只要從一個系統（一家飯店）中讀取一張鑰匙卡，即可對該系統的任何門展開攻擊，不管是駭客自己房間的鑰匙卡，或者是從快速結帳的鑰匙卡箱中所取得的鑰匙都可以。繼之駭客即可透過任何MIFARE Classic卡及任何可將資料寫入這些卡的工具來建立偽造的鑰匙卡，再利用這對偽造的鑰匙卡打開同一系統上的任何門。可用來的工具包括Proxmark3、Flipper Zero，或是支援NFC的Android手機。 更新之所以進度緩慢的主要原因為所有的鎖都必須更新軟體或直接更換，而且所有的鑰匙卡都必須重新發行，前臺軟體及卡片編碼器也必須升級，而且也可能需要升級與第三方設備的整合，如電梯、停車場或支付系統等。 儘管迄今並未收到任何攻擊報告，但研究人員指出，dormakaba於1988年便開始銷售Saflok，意味著此一系列含有安全漏洞的電子鎖已被使用超過36年，有人知道並濫用它並不是不可能的事。 < Previous News Next News >