Видео с ютуба Neural Network Vulnerabilities

Deep Learning's Most Dangerous Vulnerability: Adversarial Attacks at Silicon Valley Code Camp 2019

Model Stealing Attacks Against Inductive Graph Neural Networks

Visualizing the Impact of Adversarial Attacks on Perception in Convolutional Neural Networks

32 - Neural Network Assisted Fuzzing - Discovering Software Vulnerabilities Using Deep Learning

Exploring Vulnerabilities in Spiking Neural Networks: Direct Adversarial Attacks on Raw Event Data

Backdooring and Poisoning Neural Networks with Image-Scaling Attacks

USENIX Security '24 - Hijacking Attacks against Neural Network by Analyzing Training Data

Model Stealing Attacks Against Inductive Graph Neural Networks

Security Vulnerabilities in Machine Learning

Adversarial Attacks on AI: Impact and Defenses

Graph Neural Network-based Vulnerability Predication

USENIX Security '22 - Inference Attacks Against Graph Neural Networks

VCodeDet: a Graph Neural Network for Source Code Vulnerability Detection

Evasion Attacks on Neural Networks

Adversarial Attacks On Machine Learning-Based Malware Detection Systems

Visual Analytics of Neuron Vulnerability to Adversarial Attacks on Convolutional Neural Networks

Deep Learning Based Vulnerability Detection : Are We There Yet?

LineVD: Statement-level Vulnerability Detection using Graph Neural Networks

MVD: Memory-Related Vulnerability Detection Based on Flow-Sensitive Graph Neural Networks

[ACM MTD workshop 2021] Using Honeypots to Catch Adversarial Attacks on Neural Networks