Adversarial Robustness
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Benchmarks
AdvGLUE
CIFAR-10
ImageNet
ImageNet-A
ImageNet-C
Stylized ImageNet
CIFAR-100
Most implemented
Towards Deep Learning Models Resistant to Adversarial Attacks
Certified Adversarial Robustness via Randomized Smoothing
Generating Adversarial Examples with Adversarial Networks
Theoretically Principled Trade-off between Robustness and Accuracy
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Robustness May Be at Odds with Accuracy
Papers
Controllable Stochastic Quantization Encoding for Adversarially Robust Spiking Neural Networks
Spiking Neural Networks (SNNs) have attracted increasing attention due to their impressive temporal dynamics, energy efficiency, and brain-inspired mechanisms. Although SNNs have demonstrated promising performance in ima…
Adversarial RobustnessImage ClassificationBeyond Training: A Feasibility Taxonomy for Inference-Time AI Governance
Compute governance today is a governance of training: the thresholds, reporting requirements, and frontier-AI regimes now in force attach to training compute and treat the trained model as the regulatory unit. That pictu…
Adversarial RobustnessToward Sub-1 kB Identity-Preserving Face Compression: A Benchmark of Codecs, a Custom Learned Codec, and Studies of Resolution, Demographic Fairness, Recompression, and Adversarial Robustness
Storing face images under a hard sub-kilobyte budget, as required for identity documents, smart-card biometrics and bandwidth-constrained verification, forces a codec to discard most of the signal while keeping what a fa…
Adversarial RobustnessFaults That Fortify: CNN Adversarial Robustness via GPU Undervolting
Convolutional Neural Networks (CNNs) face a dual challenge: vulnerability to adversarial attacks and prohibitive training cost. Adversarial training is effective but expensive, a burden that grows as learning shifts to t…
Adversarial RobustnessTraining-Free Reconstruction-Based AI-Generated Image Detectors Are Inherently Vulnerable to Adversarial Examples
The impressive visual quality and ubiquity of AI-generated images call for reliable and robust detection methods. Reconstruction-based detectors have emerged as a promising direction for transparent and training-free ide…
Adversarial RobustnessAdROD: HyperNetwork-based Adversarially Robust Object Detection for Autonomous Driving
Camera-based object detectors are vulnerable to physical adversarial attacks designed to suppress detections. While adversarial training and input purification offer some protection, they often overfit to specific attack…
Robust Object DetectionAdversarial RobustnessAutonomous DrivingObject Tracking