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AI security

216 articles · Page 3 of 3
NetEase Smart Enterprise Tech+
NetEase Smart Enterprise Tech+
Sep 2, 2021 · Artificial Intelligence

How AI Detects Video Deepfakes: Techniques, Challenges, and Real-World Solutions

This article explores the rapid rise of AI‑generated video deepfakes, examines the four main manipulation techniques, discusses the inherent security risks, and presents NetEase Yidun’s comprehensive detection framework—including face‑detection‑based classification, semi‑supervised learning, feature fusion, and model distillation—to combat content‑security threats.

AI securitycomputer visiondeepfake detection
0 likes · 12 min read
How AI Detects Video Deepfakes: Techniques, Challenges, and Real-World Solutions
Infra Learning Club
Infra Learning Club
May 19, 2021 · Cloud Computing

Why Distributed Cloud Is a Top 2020 Strategic Technology Trend

The article analyzes distributed cloud as a breakthrough cloud model that defines service location, bridges gaps in hybrid cloud, evolves through four phases, and underpins emerging use cases such as edge, blockchain, and AI security, offering enterprise architects strategic guidance for 2024 and beyond.

AI securityBlockchainCloud Computing
0 likes · 21 min read
Why Distributed Cloud Is a Top 2020 Strategic Technology Trend
Tencent Tech
Tencent Tech
May 13, 2021 · Artificial Intelligence

Seeing Inside the Black Box: Visualizing Neural Network Training and Adversarial Threats

This article explains how neural networks work, walks through the step‑by‑step training process of a convolutional model, showcases vivid visualizations of each layer, and demonstrates how tiny adversarial perturbations can dramatically alter predictions, highlighting the importance of AI security.

AI securityCNN visualizationadversarial examples
0 likes · 6 min read
Seeing Inside the Black Box: Visualizing Neural Network Training and Adversarial Threats
Kuaishou Tech
Kuaishou Tech
Apr 6, 2021 · Artificial Intelligence

Frequency-Aware Feature Learning with Single-Center Loss for Face Forgery Detection

Researchers from USTC and Kuaishou propose a frequency‑aware feature learning framework that combines a data‑driven adaptive frequency module with a novel single‑center loss, achieving state‑of‑the‑art performance on deepfake detection while addressing class‑distribution challenges.

AI securitycomputer visiondeepfake detection
0 likes · 7 min read
Frequency-Aware Feature Learning with Single-Center Loss for Face Forgery Detection
Architects Research Society
Architects Research Society
Nov 29, 2020 · Information Security

AI and Machine Learning Threats to Autonomous Vehicles and Drones: Security Risks and Attack Vectors

A UN, Interpol and Trend Micro report warns that cyber criminals can exploit artificial intelligence and machine learning to launch attacks on autonomous cars, drones and IoT vehicles, potentially causing physical harm, traffic disruption, and data theft, highlighting urgent security challenges for emerging technologies.

AI securityAutonomous VehiclesIoT threats
0 likes · 5 min read
AI and Machine Learning Threats to Autonomous Vehicles and Drones: Security Risks and Attack Vectors
Tencent Tech
Tencent Tech
Sep 25, 2020 · Artificial Intelligence

What’s Inside Tencent’s AI Security Attack Matrix? A Minefield Guide

Tencent’s AI Security Attack Matrix, the industry’s first AI‑focused risk framework, maps attack tactics, techniques, and processes across the AI lifecycle, offering practical guidance for researchers and developers to identify and mitigate security threats in AI systems.

AI safetyAI securityTencent
0 likes · 5 min read
What’s Inside Tencent’s AI Security Attack Matrix? A Minefield Guide
AntTech
AntTech
Aug 18, 2020 · Artificial Intelligence

Shared Intelligence vs. Federated Learning: Techniques, Challenges, and Ant Group’s Practical Experience

The article compares shared intelligence and federated learning, examines privacy‑preserving techniques such as MPC, TEE, and differential privacy, discusses gradient‑inversion attacks and their mitigations, and presents Ant Group’s end‑to‑end system design and real‑world deployments in finance.

AI securityAnt GroupDifferential Privacy
0 likes · 22 min read
Shared Intelligence vs. Federated Learning: Techniques, Challenges, and Ant Group’s Practical Experience
AntTech
AntTech
Jun 2, 2020 · Artificial Intelligence

Privacy-Preserving Machine Learning Workshop at CCS 2020 (Ant Shared Intelligence)

The Ant Shared Intelligence workshop at ACM CCS 2020 invites researchers and practitioners to submit short papers on privacy‑preserving machine learning techniques such as secure multi‑party computation, homomorphic encryption, differential privacy, federated learning, and related applications, with a submission deadline of June 21, 2020.

AI securityCCS2020Differential Privacy
0 likes · 5 min read
Privacy-Preserving Machine Learning Workshop at CCS 2020 (Ant Shared Intelligence)
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 11, 2019 · Artificial Intelligence

How Adversarial Attacks Threaten AI: Real-World Cases & Alibaba’s Defense

AI brings convenience but also new security challenges; this article explains the two main sources of AI safety issues, details adversarial example techniques, showcases applications such as face‑recognition attacks and robust captcha designs, and highlights Alibaba’s research and the IJCAI‑19 AI adversarial competition.

AI securityadversarial examplescaptcha
0 likes · 8 min read
How Adversarial Attacks Threaten AI: Real-World Cases & Alibaba’s Defense
JD Tech
JD Tech
Dec 10, 2018 · Information Security

Container Sandbox for Contextual Behavior Analysis Presented at BlackHat Europe

JD Security’s Silicon Valley AI security scientist unveiled a novel container‑based sandbox at BlackHat Europe, detailing how contextual behavior analysis can detect and trace malicious code by leveraging lightweight containers, improving threat detection speed and accuracy for enterprise defenses.

AI securitySandboxbehavior analysis
0 likes · 6 min read
Container Sandbox for Contextual Behavior Analysis Presented at BlackHat Europe
AntTech
AntTech
Nov 1, 2018 · Artificial Intelligence

Heterogeneous Graph Neural Networks for Malicious Account Detection (GEM) – Overview of Ant Financial’s CIKM 2018 Paper

This article introduces the GEM method, the first heterogeneous graph neural network designed for malicious account detection, explains the nature and characteristics of malicious accounts, describes why graph neural networks are effective, and presents experimental results from the authors' CIKM 2018 study.

AI securityCIKM 2018financial fraud
0 likes · 8 min read
Heterogeneous Graph Neural Networks for Malicious Account Detection (GEM) – Overview of Ant Financial’s CIKM 2018 Paper
JD Tech
JD Tech
Sep 7, 2018 · Information Security

Big Data and AI Security Insights from ISC 2018 Conference

The ISC 2018 conference highlighted the growing importance of big data and artificial intelligence security, presenting JD's research on anti‑scraping techniques, AI‑driven defenses against black‑market attacks, and a service‑oriented approach to protecting user data across enterprises.

AI securityBig Dataanti-scraping
0 likes · 5 min read
Big Data and AI Security Insights from ISC 2018 Conference
JD Tech
JD Tech
Aug 20, 2018 · Artificial Intelligence

Understanding AI Black‑Box Risks and Security: From Adversarial Samples to JD's Explainable AI Solution

The article explains how the black‑box nature of deep learning creates security risks such as adversarial attacks, describes real‑world examples in autonomous driving and medical imaging, and showcases JD Security's explainable AI system that demystifies model decisions to improve AI safety and industry adoption.

AI securityJD Securityadversarial examples
0 likes · 11 min read
Understanding AI Black‑Box Risks and Security: From Adversarial Samples to JD's Explainable AI Solution