Papers Adversarial Robustness
“Adversarial Robustness” 태그가 달린 논문 2,130편 · 필터 해제
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 TrackingA Four-Axis Trustworthiness Benchmark for LLM-as-Judge in Principle-Based Regulation
Principle-based regulation, with evaluative standards such as "fair, clear, and not misleading" or "deliver good outcomes", cannot be reduced to binary predicates, and LLM-as-judge is increasingly used as the substitute.…
Adversarial RobustnessUniversal Concept Disruption for SAM3 Image Segmentation
SAM3 extends promptable segmentation from geometry-driven mask prediction to open-vocabulary concept segmentation, where a text-conditioned grounding model decides whether a concept is present and segments all matching i…
Adversarial RobustnessImage SegmentationAdversarial AttackReACT-CLIP: Response-Aware Test-Time Defense for Vision--Language Models
Training-free test-time defenses offer a practical way to improve the adversarial robustness of CLIP-style vision--language models without modifying the pretrained model. However, their correction strength is typically f…
Adversarial RobustnessThe Noise Premium in Adversarial Training for Kernel Regression
Adversarial training can improve the robustness of predictive models to bounded perturbations, often at the cost of statistical efficiency. We study this trade-off in kernel regression over a reproducing kernel Hilbert s…
Adversarial RobustnessUnifying Adversarially Robust Model Experts in Vision-Language Models
Vision-language models (VLMs), such as CLIP, are vulnerable to adversarial attacks, posing a serious problem for real-life applications and deployment. Adversarial fine-tuning emerges as a prominent defense method; howev…
Adversarial RobustnessImage ClassificationZAPs: A Reward Attribution Framework for DeFi Ecosystems with Adversarial-Robust Scoring via Parallel Anomaly Ensemble Detection
Incentive programs are central to user acquisition in decentralized finance, but many reward systems rely on raw volume, transaction count, and wallet count, making them vulnerable to bots and sybil operations. We presen…
Adversarial RobustnessRevisiting the Adversarial Robustness of Graph-Based Traffic Forecasting
Traffic forecasting by graph-based AI is a critical component of intelligent transportation systems, motivating security research on robustness to malicious sensor readings. We argue that prior robustness evaluations are…
Adversarial RobustnessEvaluation of Adversarial Robustness in Arabic Language Models
The emergence of the recent outstanding capabilities of Arabic Language Models has opened doors for exposing their vulnerabilities. One of the major security risks associated with such Natural Language Processing models …
Adversarial RobustnessSemantic SimilarityAdversarial Frontiers: Minimum-Norm Attack Ensembles for Robustness Evaluation
Adversarial robustness is commonly evaluated with predefined attack ensembles, such as AutoAttack, at a single perturbation budget $\varepsilon$ and on a selective choice of perturbation norms. We argue this formulation …
Adversarial RobustnessVanillaBench: The Hidden Accuracy Cost of Adversarial Robustness
Adversarial robustness research has produced hundreds of defended models over the past decade, yet the literature almost universally reports robustness results in isolation: standard (clean) accuracy and adversarial accu…
Adversarial RobustnessReducing information dependency does not cause training data privacy. Adversarially non-robust features do
In this paper, we challenge the prevailing view that information dependency (including rote memorization) drives training data exposure to image reconstruction attacks. We show that extensive exposure can persist without…
Adversarial RobustnessImage ReconstructionRobustifying Vision-Language Models via Test-Time Prompt Adaptation
Pre-trained Vision-Language Models (VLMs) such as CLIP achieve strong zero-shot generalization, but their performance degrades sharply under adversarial perturbations. Existing test-time adaptation methods typically rely…
Zero-shot GeneralizationAdversarial RobustnessTest-time AdaptationFormal Mechanisms for Market Stability in Self-Interested Agent Societies: A Marketplace Simulation Study
Self-interested agents, left unconstrained, tend toward defection in repeated social dilemmas, causing cooperative gains from trade to collapse. This paper investigates what formal mechanisms, layered on top of unrestric…
Adversarial RobustnessAdversarial AttackStructural Adversarial Attacks on Relational Deep Learning under Integrity Constraints
Relational Deep Learning (RDL) has become a standard methodology for machine learning on relational databases: the database is encoded as a heterogeneous temporal graph in which tuples become nodes and primary-key to for…
Adversarial RobustnessGraph Neural Network