Face-Feature Tuning is a label-free logit remapping method that reduces FPR/TPR gaps across groups in deepfake detection while preserving overall accuracy.
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Robustbench: a stan- dardized adversarial robustness benchmark
17 Pith papers cite this work, alongside 116 external citations. Polarity classification is still indexing.
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ACO-MoE recovers 95.3% of clean-input performance in visual control tasks under Markov-switching corruptions by routing restoration experts and anchoring representations to clean foreground masks.
A test-time adaptation framework anchors adversarial training to a non-robust teacher's predictions, yielding more stable optimization and better robustness-accuracy trade-offs than standard self-consistency methods.
CreTTA reformulates test-time adaptation of marginal distributions as residual energy learning, producing a contrastive objective that cancels the partition function and uses relative energy differences for adaptive gradient reweighting to avoid overfitting.
RoAd-RL is a new benchmarking library for adversarial reinforcement learning that evaluates DQN, PPO, and SAC agents across 192 attack-defense configurations and finds substantial robustness variations plus cases where defenses harm performance more than attacks.
Identifies sensitivity as the source of both discriminability and vulnerability in FC classifiers versus robustness in l2 classifiers, and introduces HPM prototype fusion plus MSA evaluation to improve adversarial robustness.
Sub-network Laplace approximations always underestimate an idealized predictive variance, and the proposed gradient- and greedy-based parameter selection rules provably close that gap better than existing heuristics.
A provable adversarial noise amplification theorem under sufficient conditions enables a custom-trained detector that identifies adversarial examples at inference time using enhanced layer-wise noise signals.
RIA uses adversarial exploration of counterfactual graph environments via label-invariant augmentations to improve OoD generalization in graph classification tasks.
Compression acts as an adversarial amplifier by reducing the decision space of image classifiers, making attacks in compressed representations substantially more effective than pixel-space attacks under the same perturbation budget.
Multimodal foundation models achieve respectable but sub-specialist performance on semantic vision tasks and weaker results on geometric tasks when evaluated through prompt chaining on established benchmarks.
SmoothLLM mitigates jailbreaking attacks on LLMs by randomly perturbing multiple copies of a prompt at the character level and aggregating the outputs to detect adversarial inputs.
LGAP generates captions from images to guide diffusion-based purification, outperforming other adversarial defenses without specialized training.
The paper presents a roadmap that identifies four unsolved problems in ML safety: robustness against hazards, monitoring for hazards, alignment of model goals with human intent, and systemic safety.
A preprocessor of Gaussian noise plus bilateral filtering yields supralinear adversarial robustness in CNNs and, when paired with adversarial training, ranks near the top of RobustBench while using far less compute, parameters, epochs, and data than prior defenses.
Perceptual quality metrics correlate strongly with each other but show minimal correlation with attack success rate across medical imaging models and datasets, making ASR alone inadequate for assessing adversarial robustness.
LLM safety evaluations are hindered by noise in dataset curation, automated red-teaming, response generation, and LLM-judge evaluation, making fair comparisons difficult and slowing progress.
citing papers explorer
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Toward Calibrated, Fair, and accurate Deepfake Detection
Face-Feature Tuning is a label-free logit remapping method that reduces FPR/TPR gaps across groups in deepfake detection while preserving overall accuracy.
-
Agent-Centric Observation Adaptation for Robust Visual Control under Dynamic Perturbations
ACO-MoE recovers 95.3% of clean-input performance in visual control tasks under Markov-switching corruptions by routing restoration experts and anchoring representations to clean foreground masks.
-
Learning Robustness at Test-Time from a Non-Robust Teacher
A test-time adaptation framework anchors adversarial training to a non-robust teacher's predictions, yielding more stable optimization and better robustness-accuracy trade-offs than standard self-consistency methods.
-
Contrastive Residual Energy Test-time Adaptation
CreTTA reformulates test-time adaptation of marginal distributions as residual energy learning, producing a contrastive objective that cancels the partition function and uses relative energy differences for adaptive gradient reweighting to avoid overfitting.
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RoAd-RL: A Unified Library and Benchmark for Robust Adversarial Reinforcement Learning
RoAd-RL is a new benchmarking library for adversarial reinforcement learning that evaluates DQN, PPO, and SAC agents across 192 attack-defense configurations and finds substantial robustness variations plus cases where defenses harm performance more than attacks.
-
Sensitivity as a Double-Edged Sword: A Trade-off Between Discriminability and Adversarial Robustness
Identifies sensitivity as the source of both discriminability and vulnerability in FC classifiers versus robustness in l2 classifiers, and introduces HPM prototype fusion plus MSA evaluation to improve adversarial robustness.
-
Optimality of Sub-network Laplace Approximations: New Results and Methods
Sub-network Laplace approximations always underestimate an idealized predictive variance, and the proposed gradient- and greedy-based parameter selection rules provably close that gap better than existing heuristics.
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Detecting Adversarial Data via Provable Adversarial Noise Amplification
A provable adversarial noise amplification theorem under sufficient conditions enables a custom-trained detector that identifies adversarial examples at inference time using enhanced layer-wise noise signals.
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Adversarial Label Invariant Graph Data Augmentations for Out-of-Distribution Generalization
RIA uses adversarial exploration of counterfactual graph environments via label-invariant augmentations to improve OoD generalization in graph classification tasks.
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Compression as an Adversarial Amplifier Through Decision Space Reduction
Compression acts as an adversarial amplifier by reducing the decision space of image classifiers, making attacks in compressed representations substantially more effective than pixel-space attacks under the same perturbation budget.
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How Well Does GPT-4o Understand Vision? Evaluating Multimodal Foundation Models on Standard Computer Vision Tasks
Multimodal foundation models achieve respectable but sub-specialist performance on semantic vision tasks and weaker results on geometric tasks when evaluated through prompt chaining on established benchmarks.
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SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks
SmoothLLM mitigates jailbreaking attacks on LLMs by randomly perturbing multiple copies of a prompt at the character level and aggregating the outputs to detect adversarial inputs.
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Language Guided Adversarial Purification
LGAP generates captions from images to guide diffusion-based purification, outperforming other adversarial defenses without specialized training.
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Unsolved Problems in ML Safety
The paper presents a roadmap that identifies four unsolved problems in ML safety: robustness against hazards, monitoring for hazards, alignment of model goals with human intent, and systemic safety.
-
A combination of noise and bilateral filters achieve supralinear and scalable adversarial robustness in CNNs
A preprocessor of Gaussian noise plus bilateral filtering yields supralinear adversarial robustness in CNNs and, when paired with adversarial training, ranks near the top of RobustBench while using far less compute, parameters, epochs, and data than prior defenses.
-
Beyond Attack Success Rate: A Multi-Metric Evaluation of Adversarial Transferability in Medical Imaging Models
Perceptual quality metrics correlate strongly with each other but show minimal correlation with attack success rate across medical imaging models and datasets, making ASR alone inadequate for assessing adversarial robustness.
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LLM-Safety Evaluations Lack Robustness
LLM safety evaluations are hindered by noise in dataset curation, automated red-teaming, response generation, and LLM-judge evaluation, making fair comparisons difficult and slowing progress.