< Back 關於課程 安控產業是國家列為5大信賴產業之一，獲得政府極大重視！尤其安控系統本身的特殊性，多應用於國家基礎建設、公共場所、交通、各類建築、廠房/工廠自動化、停車場自動化及企業設施和家庭住宅等多元領域，其系統的應用除了預防犯罪、保護財產及確保人身安全之外；隨著人工智慧AI、物聯網、雲端運算及大數據技術的迅速發展，如今的安防系統不僅變得更加智能，還能自動學習並針對不同的需求、擴大到企業的智慧化營運管理！大大提高了安控(安防)系統的整體防護效率與應用價值。 然而安防系統是一個廣泛涉及電子、電機、資訊、通訊等跨領域技術的產業，目前仍缺少相對應的學校(系所)培養專業人才，再加上現今的AIoT世代，數位化與智慧化工程系統升級的要求加劇，智慧安防系統所使用的配線材料、施作方法、佈線方式和過去有明顯差異，因此更需要系統化的專業課程來引導從業人員正確認知與實作，有效地規範業者安防系統的規畫、設計及施工。 台灣智慧安防公會依據產業的需求規劃本次培訓檢定課程，提供安防從業人員未來在場域工程施作時能參考標準規範來執行。透過此標準，預期不僅可協助產業提高工程品質、更可因品質的提升獲得場域業主的認可、進而爭取更多的工程商機。 課程設計 日期 課程內容 4月26日 安防產業概述 基本電路與安防線材 網路與安全 5月3日 光纖/網路佈線 5月10日 佈線實作 監視系統 (架構/佈線/架設與設定) 5月17日 停車場管制 (設備/架構/建置) 門禁系統 (架構/辨識方法/管制模式/連線與網路運用) 5月24日 防盜系統 (設備/架構/應用) 中央監控系統 (架構/跨系統整合) 5月24日 檢定考試 開課日期： 2025年4/26(六)~5/24(六) 每周六 早上9:00~下午17:00上課，共計35小時。 包含檢定考試(不另外收費) 開課地點： 景文科技大學 電資館七樓 E710教室 地址：新北市新店區安忠路99號 交通資訊 https://reurl.cc/lNpe6d 培訓對象： 1. 安防產業從業人員在職進修 2. 保全、物業、安全管理相關從業人員有工作需求者 3. 有興趣想投入安防產業者 4. 或對本課程有興趣者，均可報名參加。 學員證書： 1. 凡完成以上課程之學員均可獲頒【結業證書】 2. 通過檢定考試之學員可獲頒【檢定合格證書】 收費標準： 1. 一般學員報名費 8,500 元/人。 2. 台灣智慧安防工業同業公會會員可享優惠，報名費 6,000 元/人。 以上收費含教材費、檢定考試費、證書及午餐等。 報名及付款方式： 請填寫報名表，以Email( tiaiss@tiaiss.org.tw )；或傳真(02-22210629)方式 回傳報名表至秘書處 請下載招生簡章 課程費用請於完成報名後2周內匯款繳交(匯款後請來電或Email通知匯款帳號後5碼)。 課程諮詢，請洽：02-2221-0617 或 Email： tiaiss@tiaiss.org.tw 台灣智慧安防工業同業公會 / 秘書處 安防工程專技人員培訓檢定課程招生簡章 .pdf 下載 PDF • 298KB 安防工程專技人員培訓檢定課程【初階班】 4/26(六)~5/24(六) 5 週/35小時 本課程受到各界熱烈迴響，截至2/10第一梯次上課名額已滿！ 我們將盡快安排第二梯次上課時間、並上網公告。 再次感謝大家的支持！

IBM 發布「量子安全就緒指數」報告 三大評估領域 : 籲企業加速強化量子安全韌性 2025-10-22 說資安新聞網 新聞來源： https://cybersecurenews.com.tw/industry-talk-082/ IBM 商業價值研究院 (IBV) 近期發布「量子安全就緒指數」報告( Quantum-Safe Readiness Index, QSRI )，呈現全球企業因應量子時代的程度與進展。 QSRI 顯示全球企業正緩步推進其量子安全韌性。IBM 建議企業應根據自身風險程度與應變能力，首先建立量子安全治理策略、架構與跨部門管理機制；透過人員再培訓計畫，彌補量子安全技能缺口；並導入可觀測工具以監控密碼風險。 為瞭解全球企業量子安全的準備程度，IBM 商業價值研究院 (IBV) 與市場分析公司明智夥伴（Phronesis Partners）合作，訪問了來自 27 個地區/市場、跨 14 個產業的 750 位負責業務、營運、安全或技術職能的企業主管 。IBM 以 2023 年首次定義的「量子安全就緒指數」(Quantum-Safe Readiness Index, QSRI) 評估與呈現全球企業規劃與部署量子安全能力的程度。 IBM 2025 「量子安全就緒指數」指出，73% 的受訪企業已經開始研究量子安全議題，但僅有19% 的企業有實際關於量子安全的近程規劃、或明確的量子安全策略與目標。造成認知與行動的差距的主要原因有二： 企業缺乏量子安全的關鍵人才與技能 ，及 量子安全議題在企業內的權責分散。 62% 的受訪企業期待由現有的資安供應商協助應對量子安全風險，56% 的企業認為量子安全僅屬技術議題； 主要負責量子安全議題的主管依序為技術長 (16%)、資安長 (11%)、研發主管 (10%) 與資訊長 (10%) 等。 IBM 2025「量子安全就緒指數」評估14項企業作為，歸納為三大領域包括：量子安全的 探索能力 、 可觀測性與監控能力 、與 轉型能力 。14 項指標依據 IBM 對於量子的專業知識與客戶經驗分組與加權，100 分為滿分。 2025年，全球企業「量子安全就緒」的平均分數為 25 分，較 2023 年的 21 分提升 4 分。分數最高的前百分之十企業被定義為「量子安全領先者」（Quantum-Safe Champions, QSCs），其分數介於 35 至 50 分；較 2023 年的最高分 44 分進步 6 分。 報告指出全球企業在「探索」與「可觀測性與監控」領域進展較快，企業正在強化識別與監控密碼風險的能力。「轉型」能力亦有進展，但絕對分數仍偏低；顯示企業關注量子安全議題的初期動能強勁，但持續投資是將準備工作轉化為實際量子安全能力的關鍵。 量子運算的能力將重塑企業保護資料與系統的方式；當量子電腦達到足夠的規模與穩定性時，多數現行的加密方法將失效。儘管具備破解現行密碼能力的量子電腦預計六年後才問世，但網路駭客採取「先竊取，後解密」(harvest now, decrypt later) — 即今日竊取加密資料，待量子技術成熟時再行破解的手法，若企業等閒視之，延遲應對，未來將面臨資料外洩、營運中斷的風險與高額補救成本。 IBM「量子安全就緒指數」評估指標，三階段 第一階段：探索能力（Discovery） 確定受影響範圍 了解加密技術使用情況 分析對加密技術的依賴程度 建立服務水平，以過渡到量子安全加密技術 辨別基礎技術 將供應商納入治理和安全管理 第二階段：可觀測性與監控能力（Observability） 建置遙測解決方案 基於 AI 的規則引導制訂決策 發展量子安全可觀測性 建置共同的、持續的治理機制 第三階段：轉型能力（Transformation） 規劃將系統過渡到新加密技術 識別支持後量子加密技術的方法和解決方案 驗證包含新加密技術的產品 測試 NIST 量子安全加密算法 建議企業應立即啟動與加速進行量子安全轉型 根據自身風險程度與應變能力，建立量子安全治理策略、架構與跨部門管理機制 盤點對現有加密技術與應用程式的依賴性；進行密碼系統盤點，找出潛在漏洞；導入可觀測性工具以監控密碼風險 透過人員再培訓計畫，彌補量子安全技能缺口 規劃並建置符合後量子 （PQC） 標準的加密技術；在過渡期間採用傳統與後量子加密（PQC）混合架構；導入加密彈性（crypto agility）方案 與產業夥伴協作，建立與運作量子安全生態系 < Previous News Next News >

第二屆第五次理監事聯席會議紀錄 2023年11月16日 上午6:00:00

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 >

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 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 香格里拉台北遠東國際大飯店怡東園 (台北市敦化南路二段201號) 2020 1218 15:30~17:00 一、「邊緣AI運算於行動影像之應用」，周俊廷顧問/台灣大學電機工程學系副教授。 二、「資安議題下的後海思時代、區塊鏈下談安防產業上鏈」，盧濟豐顧問/昇銳電子研發處處長/台灣大學電機所/網路計算暨安全實驗室。 < Previous News Next News >

< Back 第二屆第二次理監事聯席會議 彭園餐廳板橋店(新北市板橋區雙十路二段239號4樓) 2023 Feb 17 16:30~18:30 主要議案： 1、會員入會案。 2、111年度工作報告及決算報告案。 3、討論本會第二屆顧問聘任案。 4、討論本會理監事捐助案。 5、會務人員借調案，提請追認通過。 6、討論本會開辦安防案件評鑑業務項目案。 7、擬建立安防資材共同採購機制討論案(公共供應契約)。 8、商議本會開辦安防工程實務研習證照班初步構想案。 < Previous Next >

< Back 關於課程 【主辦單位】 ：台灣智慧安防工業同業公會 【合辦單位】 ：景文科技大學 【 開課日期】： 包含檢定考試(不另外收費) （一） 114 年09 月 6 日至 10月18日 (每周六)，合計35小時。 （二） 114年 11 月 8 日至 12月13日 (每周六)，合計35小時。 【開課地點】： 景文科技大學（地址：新北市新店區安忠路 99 號） 交通資訊 https://reurl.cc/lNpe6d 【 課程說明 】： 安控產業是國家列為 5 大信賴產業之一，獲得政府極大重視！尤其安控系統本身的特殊性，多應用於國家基礎建設、公共場所、交通、各類建築、廠房/工廠自動化、停車場自動化及企業設施和家庭住宅等多元領域，其系統的應用除了預防犯罪、保護財產及確保人身安全之外；隨著人工智慧 AI、物聯網、雲端運算及大數據技術的迅速發展，如今的安防系統不僅變得更加智能，還能自動學習並針對不同的需求、擴大到企業的智慧化營運管理！大大提高了安控(安防)系統的整體防護效率與應用價值。 然而安防系統是一個涉及電子、電機、資訊、通訊等跨領域技術的產業，目前仍缺少相對應的學校(系所)培養專業人才，再加上AIoT 世代，數位化與智慧化工程系統升級的速度加劇，智慧安防系統所使用的配線材料、施作方法、佈線方式和過去明顯差異，此時更需要系統化的專業課程來引導從業人員正確的學識與實作，有效地統一業者安防系統的規畫、設計及施工。 台灣智慧安防公會依據產業的需求規劃本次培訓檢定課程，提供安防從業人員未來在場域工程施作提高工程品質、更可因品質的提升獲得場域業主的認可、進而爭取更多的工程商機。 【 課程設計 】 本會保有課程內容異動與調整之權利 【 課程特色 】※ 專為安防人員量身打造的培訓計畫 ※ 由本會與景文科技大學產學合作，協助已從業或即將投入就業之安防業者認識安防系統工程的各項內容及標準規範，包含產業概述、基礎電路與線材、網路與安全、光纖與網路佈線、監視系統、停車場管制、門禁系統、防盜系統、中央監控系統等系統架構、設計規劃及施工安裝之標準化流程，此將提供從業人員安防工程施作標準參考，藉此讓安防產業之工程品質再升級。 【 培訓對象 】 ： 1. 安防產業從業人員在職進修 2. 保全、物業、安全管理相關從業人員有工作需求者 3. 有興趣想投入安防產業者 【 師 資 】 ： 1. 學識理論課程由景文科技大學安排資通訊及智慧科技專業領域講師群授課 2. 產業實務課程由安防公會聘請業界先進，專業傳授安防工程標準化架構與作業規範。 【 學員證書 】 ： 1. 凡完成以上課程(請假未超過7小時)之學員均可獲頒【結業證書】 2. 通過檢定考試之學員可獲頒【檢定合格證書】 【 收費標準 - 推廣優惠價 】 ： 1. 一般學員報名費 8,500 元/人 。(參考： 安防公會入會辦法 ) 2. 台灣智慧安防工業同業公會會員可享公會補助 30%， 會員報名費 6,000元/人 。 以上費用含教材費、檢定考試費、證書及午餐等。 【 報名及付款方式 】 ： 1. 線上報名：請掃描QR Code或網址 https://reurl.cc/knejp9 2. 付款方式：匯款-- 課程費用請於報名後 2 周內完成匯款 (匯款後請來電或 Email通知匯款帳號後 5 碼)。 匯款銀行：兆豐銀行-八德分行 匯款戶名：台灣智慧安防工業同業公會 匯款帳號：061-09-018059 【注意事項】： 1. 每梯次招收40位學員，如招生不足30位，則順延開課日期，以公會通知為準。 2. 為服務更多廠商， 每一梯次同一公司限額5名學員參加 ，超額部分順延下一梯次。 3. 每家公司補助課程報名費(每年)上限為會員所繳年費)，115年1月1日起施行。 4. 同一企業或關係企業/團報人數達30人以上可申請開設企業專班，開課日期需視本會與景文科大及講師討論後專案辦理，費用另訂。 【聯絡資訊】 ： 課程諮詢，請洽：02-22210617 或 Email： tiaiss@tiaiss.org.tw 台灣智慧安防工業同業公會 / 秘書處 下載招生簡章 1140926「安防工程專技人員培訓檢定課程—初階班」招生簡章 .pdf 下載 PDF • 615KB 安防工程專技人員培訓檢定課程【初階班】 第2、第3梯次 09/06(六)~10/18(六) 11/08(六)~12/13(六) 5 週/35小時

工研院IEKCQM：搭AI大勢 2024年製造業產值預估成長6.47% 2024-06-12 工研院、IEK產業情報網 新聞來源： https://www.itri.org.tw/ListStyle.aspx?DisplayStyle=01_content&SiteID=1&MmmID=1036276263153520257&MGID=113061215151435354 臺灣半導體業受惠於AI議題帶動相關供應鏈需求強勁，全年發展持樂觀看待，預估2024年臺灣半導體產業將首次突破新臺幣5兆大關, 預計可達新臺幣5兆1,134億元，年成長17.7%。 工研院綜整國內外政經情勢，今（12）日舉辦「2024年臺灣製造業暨半導體產業景氣展望記者會」，發布2024年臺灣製造業及半導體景氣展望預測結果。預估整體製造業產值達23.1兆元新臺幣，年增率上修為6.47%。臺灣半導體業受惠於AI議題帶動相關供應鏈需求強勁，全年發展持樂觀看待，預估2024年臺灣半導體產業將首次突破新臺幣5兆大關, 預計可達新臺幣5兆1,134億元，年成長17.7%。 工研院IEKCQM預測團隊指出，隨著就業市場穩定、通膨緩降、供應鏈問題紓解，國際需求逐漸改善，全球製造業景氣已緩步回暖。在國際需求增加的推動下，國內民眾消費成長延續，民間投資意願轉強，輔以AI伺服器新商機及股市創高，我經濟表現料將溫和穩健。惟各國財政支持政策退場、地緣政治緊張、氣候風險加劇等因素，將持續對全球中長期經濟增長帶來挑戰，仍需審慎留意。 通膨降溫、國際需求回升，四大業別均重返成長 金屬機電： 高通膨、高利率政策的滯後影響、地緣政治的不確定性、中國房地產投資持續下滑，但基建投資需求仍在及製造業回暖，有利支撐產業成長。通膨、日圓貶值、地緣政治等不利因素延續，然訂單年增率已見回升、輔以新興產業應用帶來創新市場，預期產業景氣將緩步改善。預估2024年金屬機電業產值將成長1.92%。 資訊電子： 供應鏈庫存去化落底，全球手機、PC等終端消費性電子市場需求可望逐步反轉，車用電子亦於持續調整階段；輔以低基期因素，產業景氣展望正向。AI需求擴張，Al伺服器與邊緣AI終端裝置應用快速的發展，帶動半導體在AI、HPC先進製程的產能持續擴增以滿足市場需求，並推進電子零組件及材料產值成長。預估2024年資訊電子業產值將成長11.09%。 化學工業： 區域戰爭、OPEC+延長額外減產計畫，油價支撐石化產品價格，惟市場需求保守、中國新增產能市場競爭，限縮石化業成長空間。國際淨零永續趨勢，帶動臺灣石化產業朝向綠色製造、高值差異化商品、碳循環再利用技術發展。預估2024年化學工業產值將成長2.31%。 民生工業： 歐美品牌廠去庫存順利，通膨及升息限縮消費支出的效應緩步淡化，國際市場消費信心回升，通路與品牌商下單意願轉強，有利紡織產業逐季復甦國際觀光客回流、就業市場持續改善、股市創高帶來的財富效應，帶動外食、旅遊、休閒娛樂消費，在高基期下，民眾消費意願續強，支撐我民生工業成長。預估2024年民生工業產值將成長4.33%。 資料來源：工研院產科國際所（2024/06） IEKView：國際三大趨勢成形，建議鞏固AI時代下的半導體技術優勢、以科技建構韌性社會、並打造臺灣成全球高科技生產管理/研發基地 工研院觀察，現國際有三大趨勢已然成形，成為2024年影響臺灣製造業發展的關鍵： 第一，萬物皆AI 時代來臨，AI帶動半導體產業先進製程需求。 晶片技術高速演進，促進了AI、下世代通訊等新興科技的崛起與普及，為各行各業的創新應用帶來了強勁驅動力。隨著AI正由雲端走向邊緣（Edge AI），加速「萬物皆AI 時代（AI for all）」來臨，臺灣憑藉半導體、伺服器、記憶體及其他ICT產業之國際競爭優勢，未來發展可期。 第二，世界風險前景惡化，影響社會可持續發展。 2024年世界經濟論壇（WEF）的世界風險報告指出，AI所生成的錯誤訊息和虛假訊息，為社會帶來了不確定性，為全球短期內最大的風險。報告亦歸納，環境變遷、科技化加速、社會人口成長分岔、地緣政治等4種結構性因素，將帶來更多不確定性及動盪的可能性。 第三，全球貿易結構重建，多點製造、在地供應同時發生。 國際間貿易集中度指數下降，意味著各國的貿易夥伴日益多元，海外（off-shore）分工更分散；另一方面，臺灣本土（on-shore）製造成趨勢。經濟部統計我國近五年（2019-2023）外銷訂單海外生產比，顯示傳產貨品外移、但科技類產品回流製造的兩極化現象。 面對此三大趨勢，工研院建議，因應急遽上升的AI需求，臺灣應從人才與新創培育、鞏固領先地位等兩大面向確保我國半導體產業在AI時代下的優勢。在人才新創培育方面，應建立人才循環方略及新創孵育作法；也需深化產業基礎科技，並發展AI輔助IC設計以尋求技術新突破，才能長久鞏固臺灣的領先地位。 在社會穩定發展的議題上，臺灣面臨多型態的風險衝擊，應嘗試透過新興科技技術強化韌性，從生產力韌性、基礎設施韌性及能資源韌性等三大應用領域切入。此外，為適應全球貿易結構重建，建議政府可透過：支持業界研發及進行人才布局、協助產業擴大外商合作、設立研發管理總部、切入國際供應鏈等四大路徑，將臺灣打造成全球高科技生產管理/研發基地。 對半導體產業最新觀察 工研院預測：2024年臺灣半導體產業發展持樂觀看待，主要因為通膨降溫，整體就業市場也趨於穩定，消費力回升，以及產業面的庫存調整大致完成，再加上AI帶動對半導體需求增加。 2024年臺灣半導體產業，預計因為通膨降溫及庫存回到合理水位，加上AI帶動相關應用產品對半導體的需求，預估2024年臺灣半導體產業將達新臺幣5兆1,134億元，年成長17.7%，優於2024年全球半導體成長的13.1%。 另外，2024年臺灣IC設計業因為通膨趨緩加上AI手機、AI PC需求，預計2024年將成長15.1%。而臺灣IC製造業也因為先進製程產出持續提升，DRAM價格回昇，預計2024年成長20.2%。臺灣IC封測業也受惠換機需求與高階封測需求，除了資本投資大幅成長，也布局晶片異質整合與高階封裝技術，預計2024年成長11.4%。 IEKView：2024年AI發展將從雲端走向終端，AI PC與AI手機將成為GAI普及的關鍵應用，臺灣半導體產業應把握此機會，佈局相關技術與產品 隨著ChatGPT風行，各大PC品牌廠於紛紛推出AI效能的PC，搭載神經網絡的處理器（NPU），將不需透過雲端執行運算，使運算速度更高效且低延遲，語音辨識、圖像處理等功能也更智慧化。因此，AI PC占整體PC出貨量比重，預估從2024年占比二成將快速成長到2028年的占比八成。 除了AI PC之外，另一個重點是智慧型手機，由於智慧手機具備強大裝置端測算力、最多用戶及黏著度、完整應用生態的三大優勢可用於驅動GAI發展，AI手機占整體智慧手機出貨量比重，預估從2024年占比二成快速成長到2028年的占比七成。 此外，終端產品對高效能的需求也推動半導體封裝技術朝高密度互連發展，除了2.5D/3DIC等封裝技術外，在成本與效能的優勢下，也驅動扇出型封裝延伸至面板級載體。透過強化晶片異質整合與高階封裝技術，以滿足裝置端AI的終端高效能應用。 (以上即為全文閱讀) 工研院IEKCQM：搭AI大勢 2024年製造業產值預估成長6.47%.pdf < Previous News Next News >

< Back 114年9月6日至10月18日 【初階班】第二梯次 景文科技大學 商管一館 C305 3F（新北市新店區安忠路99號） 114年9月6日（六）課程 114年9月6日（六）課程 【安防產業概述】、【光纖通訊及其應用】、【網路技術與通訊協定】、【網路與資安】 謝謝講師帶來精彩的課程 感謝從台中、新竹、桃園、宜蘭特地遠道而來的學員 您們的參與無疑是對安防產業最實際的支持 無形中更為安防產業注入活力與動能 另特別感謝宗亞集團、良福保全、松華國際等企業熱烈支持 ALBUM_20250906人培初階二_250908_11.jpg ALBUM_20250906人培初階二_250908_8.jpg ALBUM_20250906人培初階二_250908_1.jpg ALBUM_20250906人培初階二_250908_11.jpg 1/4

兆豐銀全台首家引進最新金融 FIDO 生物辨識驗證 2024-01-17 財經新報 新聞來源： https://finance.technews.tw/2024/01/17/fido 兆豐銀行為全台首家引進最新的金融 FIDO 生物辨識技術，未來刷卡不用再輸入OTP (一次性密碼)，確保每一筆交易的真實性，大幅降低盜刷風險，走向無密碼時代。 所謂的 FIDO 就是由非營利組織 FIDO 聯盟所訂定的一套網路識別標準，全稱為 Fast Identity Online，主要是透過公開金鑰加密(Public Key Cryptography)的架構，進行多重因素驗證（MFA），以及生物辨識登入，嚴密地保護雲端帳號的個資。 兆豐銀行表示，以往信用卡線上交易驗證時，用戶需要輸入一次性 OTP 動態密碼進行驗證，不僅必須切換手機螢幕畫面來輸入密碼，還有可能在密碼傳送或輸入的過程中，遭詐騙集團攔截或竊取，甚至在國外更是常常無法收到 OTP 動態密碼，以致線上交易無法完成。 兆豐銀行與聯合信用卡處理中心攜手合作，針對信用卡 3D 驗證系統，提供現有的 OTP 驗證方式，並新增「兆豐身分認證 App」金融 FIDO 驗證方式，只要線上刷卡交易，系統就會立即發送「兆豐身分認證 App」推播通知至綁定裝置。 兆豐銀行指出，持卡人點選推播後，即可憑人臉或指紋驗證成功，並迅速完成交易，不僅免輸入動態驗證碼，更能節省層層跳轉的不便，即便人在國外網購刷卡也沒問題，而且使用者的機密個資不再集中放在雲端服務的伺服器管理，更能有效確保資料安全。 （首圖來源：兆豐銀行） < Previous News Next News >

< Back 114.0712【初階班】宗亞專班 景文科技大學 商館二館 A405 4F（新北市新店區安忠路99號） 114年7月12日（六）課程 114年7月12日（六）課程 安防工程專技人員培訓檢定課程_初階宗亞專班 開課了~ 感謝宗亞資訊工業股份有限公司 李舜得董事長 親自蒞臨致詞勉勵學員 114年7月12日 (六) 課程：【安防產業概述】、【基本電學及安防線材】、【網路技術與通訊協定】、【網路安全及防火牆實作】 114年7月19日 (六) 課程：【佈線實作】、【光纖通訊及其應用網路與資安】 114年8月2日 (六) 課程：【監視系統】、【監視系統實作】、【停車場管制】 114年8月9日 (六) 課程：【防盜系統】、【門禁系統】 114年8月16日 (六) 課程：【中央監控系統】、【檢核考試】 特別感謝 #景文科技大學 提供專業電腦教室， 讓安防工程專技人員培訓檢定課程_初階宗亞專班 更具專業感與質感升級！ BB456FA473D9.jpg 250712_7.jpg 20250816_11.jpg BB456FA473D9.jpg 1/20