Adversa AI
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소개:
신뢰할 수 있고 안전한 AI 시스템에 초점을 맞춘 AI 보안 솔루션.
추가됨:
8월 18 2024
월간 방문자 수:
6.9K
소셜 및 이메일:
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Adversa AI이란 무엇인가요?
Adversa AI는 사이버 보안 회사로, AI 시스템을 위협으로부터 보호하고 다양한 산업에서 신뢰성을 보장하는 안전한 AI 솔루션에 전문화되어 있습니다. 금융, 자동차 등 다양한 산업의 AI 관련 위험 및 취약성을 최소화하기 위한 평가, 인식 교육 및 자문 서비스를 제공합니다.
Adversa AI을 어떻게 사용하나요?
웹사이트를 탐색하여 Adversa AI가 제공하는 AI 보안 서비스, 평가 및 최신 연구를 탐색합니다. 사용자는 평가를 요청하고 AI 구현을 안전하게 하는 통찰력을 얻을 수 있습니다.
Adversa AI의 핵심 기능
안전한 AI 평가
AI 위험에 대한 인식 교육
AI 보안에 대한 연구 및 자문
Adversa AI의 사용 사례
#1
산업의 AI 응용 프로그램을 평가하고 보안하여 사이버 위협을 방지합니다.
Adversa AI의 FAQ
Adversa AI는 어떤 산업에 서비스를 제공하나요?
내 AI 시스템의 보안을 어떻게 평가할 수 있나요?
Adversa AI는 어떤 유형의 교육을 제공하나요?
Adversa AI 회사
Adversa AI 회사 이름: Adversa AI .
Adversa AI에 대해 자세히 알아보려면 회사 소개 페이지(https://adversa.ai/about-us/)를 방문하세요. .
Adversa AI 리뷰(0)
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Adversa AI 분석
Adversa AI 웹사이트 트래픽 분석
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상위 5지역
26.44%
14.54%
12.29%
11.93%
10.68%
May 2024 - Jan 2025 데스크톱 장치만 해당
Adversa AI 삽입 실행
웹사이트 배지를 사용하여 커뮤니티에서 Toolify Launch에 대한 지원을 유도하세요. 홈페이지나 바닥글에 쉽게 삽입할 수 있습니다.
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소셜 리스닝
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Surviving in the AI Era: Adversarial Attacks 🎭🤖
Hello Beyonders! From fashion items that render you "invisible" to surveillance to audio trickery, this video covers it all. We explore adversarial methods in artificial intelligence and the future challenges in making AI foolproof. | Artificial Intelligence, Adversarial Learning, Token Smuggling Chapters 00:00 Introduction 01:43 AI Adversarial Attacks 02:05 AI Image Attacks 04:19 AI Attacks with Fashion 07:00 AI Text Hacking Methods 09:16 AI Adversarial Methods for Audio Don't forget to LIKE & SUBSCRIBE 👍 TURN ON Notifications, and be the first to watch every video! 🔔 Subscribe to Beyond TodAI Website 🌐 https://beyondtodai.com/ Join our social media community 😊 Twitter: https://twitter.com/BeyondTodAI TikTok: https://www.tiktok.com/@beyondtodai Resources TnT Attacks! Universal Naturalistic Adversarial Patches https://tntattacks.github.io/ Security News This Week: A Tiny Piece of Tape Tricked Teslas Into Speeding Up 50 MPH https://www.wired.com/story/tesla-speed-up-adversarial-example-mgm-breach-ransomware/ One Pixel Attack for Fooling Deep Neural Networks https://arxiv.org/pdf/1710.08864.pdf https://www.bbc.com/news/technology-41845878 GPT Prompt Using 'Token Smuggling' Really Does Jailbreak GPT-4 https://www.piratewires.com/p/gpt4-token-smuggling GPT4 JAILBREAK ZOO https://adversa.ai/blog/ai-red-teaming-llm-for-safe-and-secure-ai-gpt4-and-jailbreak-evaluation/ Audio Adversarial Examples https://nicholas.carlini.com/code/audio_adversarial_examples Fashion Adversarial Methods https://adam.harvey.studio/cvdazzle https://yr.media/tech/guide-to-anti-surveillance-fashion/ https://www.wired.com/2013/10/thwart-facebooks-creepy-auto-tagging-with-these-bizarre-t-shirts/ http://www.urmesurveillance.com/ Music By: Nebular Focus - Dan Henig Jessica - Joey Pecoraro Connection - Wayne Jones Minimal Corporate Background No Copyright Music - Alexi Action All images, music and footage licensed under Creative Commons "Attribution 3.0" https://creativecommons.org/licenses/by/3.0/ Image Source: http://www.urmesurveillance.com/
EP84 How to Secure Artificial Intelligence (AI): Threats, Approaches, Lessons So Far
Guest: • Alex Polyakov (https://www.linkedin.com/in/alex-polyakov-56220a31/) , CEO of Adversa.ai (https://adversa.ai) Topics: • You did research by analyzing 2000 papers on AI attacks released in the previous decade. What are the main insights? • How do you approach discovering the relevant threat models for various AI systems and scenarios? • Which threats are real today vs in a few years? • What are the common attack vectors? What do you see in the field of supply chain attacks on AI, software supply, data? • All these reported cyberphysical attacks on computer vision, how real are they, and what are the possible examples of exploitation? Are they a real danger to people? • What are the main differences between protecting AI vs protecting traditional enterprise applications? • Who should be responsible for Securing AI? What about for building trustworthy AI? • Given that the machinery of AI is often opaque, how to go about discovering vulnerabilities? Is there responsible disclosure for AI vulnerabilities, such as in open-source models and in public APIs? • What should companies do first, when embarking on an AI security program? Who should have such a program? Resources: • “EP52 Securing AI with DeepMind CISO” (https://cloud.withgoogle.com/cloudsecurity/podcast/ep52-securing-ai-with-deepmind-ciso/) (ep52) • “EP68 How We Attack AI? Learn More at Our RSA Panel!” (https://cloud.withgoogle.com/cloudsecurity/podcast/ep68-how-we-attack-ai-learn-more-at-our-rsa-panel/) (ep68) • Adversarial AI attacks work on Humans (https://arxiv.org/pdf/1802.08195.pdf) (!) • “Maverick* Research: Your Smart Machine Has Been Conned! Now What?” (https://www.gartner.com/document/3151335) (2015) • “The Road to Secure and Trusted AI” (https://adversa.ai/report-secure-and-trusted-ai/) by Adversa AI • “Towards Trusted AI Week 37 – What are the security principles of AI and ML?” (https://adversa.ai/blog/towards-trusted-ai-week-37-what-are-the-security-principles-of-ai-and-ml/) • Adversa AI blog (https://adversa.ai/topic/trusted-ai-blog/) • AIAAIC Repository (https://www.aiaaic.org/aiaaic-repository) • Machine Learning Security Evasion Competition at MLSec (https://mlsec.io/)