Papers Adversarial Defense
“Adversarial Defense” 태그가 달린 논문 438편 · 필터 해제
Robust CurveMoE: Multi-Norm Adversarial Defense for Mixture-of-Experts Models via Mode Connectivity
Multi-norm adversarial defense aims to protect neural networks against perturbations defined by different norm constraints, but existing methods typically optimize competing robustness objectives within a single paramete…
Adversarial DefenseLarge Language Models (LLMs) and Generative AI in Cybersecurity and Privacy: A Survey of Dual-Use Risks, AI-Generated Malware, Explainability, and Defensive Strategies
Large Language Models (LLMs) and generative AI (GenAI) systems, such as ChatGPT, Claude, Gemini, LLaMA, Copilot, Stable Diffusion by OpenAI, Anthropic, Google, Meta, Microsoft, Stability AI, respectively, are revolutioni…
Adversarial DefenseFederated LearningAnomaly DetectionCode GenerationFleet: Few Shots Lead Effective AI-generated Image Detection
AI-generated image (AIGI) detection is undergoing a critical transition from laboratory benchmarks to open-world adversarial defense. The prevalent paradigm focuses on finding static feature spaces, assuming that some in…
Zero-shot GeneralizationAdversarial DefenseSemantic Smoothing via Novel View Synthesis for Robust SAR Image Classification
Deep neural networks are vulnerable to adversarial perturbations, limiting deployment in safety-critical applications such as synthetic aperture radar (SAR) automatic target recognition (ATR). Randomized smoothing improv…
Novel View SynthesisImage ClassificationAdversarial DefenseGuaranteed Jailbreaking Defense via Disrupt-and-Rectify Smoothing
This paper proposes a guaranteed defense method for large language models (LLMs) to safeguard against jailbreaking attacks. Drawing inspiration from the denoised-smoothing approach in the adversarial defense domain, we p…
Adversarial DefenseEnhancing Adversarial Robustness in Network Intrusion Detection: A Layer-wise Adaptive Regularization Approach
The new wave of adversarial attacks that utilize gradient-related vulnerabilities in neural network-based classifiers makes Network Intrusion Detection Systems more open to such threats. Although state-of-the-art adversa…
Network Intrusion DetectionAdversarial RobustnessAdversarial DefenseMemory Efficient Full-gradient Attacks (MEFA) Framework for Adversarial Defense Evaluations
This work studies the robust evaluation of iterative stochastic purification defenses under white-box adversarial attacks. Our key technical insight is that gradient checkpointing makes exact end-to-end gradient computat…
Adversarial DefenseDetecting Adversarial Data via Provable Adversarial Noise Amplification
The nonuniform and growing impact of adversarial noise across the layers of deep neural networks has been used in the literature, without a formal mathematical justification, to detect adversarial inputs and improve robu…
Adversarial DefenseCertified vs. Empirical Adversarial Robust-ness via Hybrid Convolutions with Attention Stochasticity
We introduce Hybrid Convolutions with Attention Stochasticity (HyCAS), an adversarial defense that narrows the long-standing gap between provable robustness under L2 certificates and empirical robustness against strong L…
Adversarial RobustnessAdversarial DefenseTopFeaRe: Locating Critical State of Adversarial Resilience for Graphs Regarding Topology-Feature Entanglement
Graph adversarial attacks are usually produced from the two perspectives of topology/structure and node feature, both of them represent the paramount characteristics learned by today's deep learning models. Although some…
Adversarial DefenseAll Vehicles Can Lie: Efficient Adversarial Defense in Fully Untrusted-Vehicle Collaborative Perception via Pseudo-Random Bayesian Inference
Collaborative perception (CP) enables multiple vehicles to augment their individual perception capacities through the exchange of feature-level sensory data. However, this fusion mechanism is inherently vulnerable to adv…
Adversarial DefenseBayesian InferenceZero-Sacrifice Persistent-Robustness Adversarial Defense for Pre-Trained Encoders
The widespread use of publicly available pre-trained encoders from self-supervised learning (SSL) has exposed a critical vulnerability: their susceptibility to downstream-agnostic adversarial examples (DAEs), which are c…
Self-Supervised LearningAdversarial RobustnessAdversarial DefenseAdversarial Defense in Vision-Language Models: An Overview
The widespread use of Vision Language Models (VLMs, e.g. CLIP) has raised concerns about their vulnerability to sophisticated and imperceptible adversarial attacks. These attacks could compromise model performance and sy…
Test-time AdaptationAdversarial DefenseIoT-based Android Malware Detection Using Graph Neural Network With Adversarial Defense
Since the Internet of Things (IoT) is widely adopted using Android applications, detecting malicious Android apps is essential. In recent years, Android graph-based deep learning research has proposed many approaches to …
Graph Neural NetworkAdversarial DefenseMalware DetectionHow a Bit Becomes a Story: Semantic Steering via Differentiable Fault Injection
Hard-to-detect hardware bit flips, from either malicious circuitry or bugs, have already been shown to make transformers vulnerable in non-generative tasks. This work, for the first time, investigates how low-level, bitw…
Adversarial DefenseImage CaptioningFeatureLens: A Highly Generalizable and Interpretable Framework for Detecting Adversarial Examples Based on Image Features
Although the remarkable performance of deep neural networks (DNNs) in image classification, their vulnerability to adversarial attacks remains a critical challenge. Most existing detection methods rely on complex and poo…
Computational EfficiencyImage ClassificationAdversarial DefenseDynamic Black-box Backdoor Attacks on IoT Sensory Data
Sensor data-based recognition systems are widely used in various applications, such as gait-based authentication and human activity recognition (HAR). Modern wearable and smart devices feature various built-in Inertial M…
Human Activity RecognitionAdversarial DefenseExplainableGuard: Interpretable Adversarial Defense for Large Language Models Using Chain-of-Thought Reasoning
Large Language Models (LLMs) are increasingly vulnerable to adversarial attacks that can subtly manipulate their outputs. While various defense mechanisms have been proposed, many operate as black boxes, lacking transpar…
Adversarial DefenseDebiased Dual-Invariant Defense for Adversarially Robust Person Re-Identification
Person re-identification (ReID) is a fundamental task in many real-world applications such as pedestrian trajectory tracking. However, advanced deep learning-based ReID models are highly susceptible to adversarial attack…
Person Re-IdentificationAdversarial DefenseMetric LearningLoReTTA: A Low Resource Framework To Poison Continuous Time Dynamic Graphs
Temporal Graph Neural Networks (TGNNs) are increasingly used in high-stakes domains, such as financial forecasting, recommendation systems, and fraud detection. However, their susceptibility to poisoning attacks poses a …
Recommendation SystemsAdversarial DefenseAnomaly DetectionFraud Detection