IPG: Incremental Patch Generation for Generalized Adversarial Patch Training

• URL: http://arxiv.org/abs/2508.10946v1
• Date: Wed, 13 Aug 2025 15:53:58 GMT
• Title: IPG: Incremental Patch Generation for Generalized Adversarial Patch Training
• Abstract summary: The advent of adversarial patches poses a significant challenge to the robustness of AI models.<n>This paper proposes Incremental Patch Generation (IPG), a method that generates adversarial patches up to 11.1 times more efficiently than existing approaches.<n>The efficacy of IPG is demonstrated by experiments and ablation studies including YOLO's feature distribution visualization and adversarial training results.
• Score: 1.54369283425087
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The advent of adversarial patches poses a significant challenge to the robustness of AI models, particularly in the domain of computer vision tasks such as object detection. In contradistinction to traditional adversarial examples, these patches target specific regions of an image, resulting in the malfunction of AI models. This paper proposes Incremental Patch Generation (IPG), a method that generates adversarial patches up to 11.1 times more efficiently than existing approaches while maintaining comparable attack performance. The efficacy of IPG is demonstrated by experiments and ablation studies including YOLO's feature distribution visualization and adversarial training results, which show that it produces well-generalized patches that effectively cover a broader range of model vulnerabilities. Furthermore, IPG-generated datasets can serve as a robust knowledge foundation for constructing a robust model, enabling structured representation, advanced reasoning, and proactive defenses in AI security ecosystems. The findings of this study suggest that IPG has considerable potential for future utilization not only in adversarial patch defense but also in real-world applications such as autonomous vehicles, security systems, and medical imaging, where AI models must remain resilient to adversarial attacks in dynamic and high-stakes environments.

Related papers

• Defending against Model Extraction for GNNs with Model Reprogramming [57.82196268649236]
  Graph Neural Networks (GNNs) serve as the backbone for high-stakes applications in Machine-Learning-as-a-Service (ML)<n>Still, their black-box deployment exposes them to Model Extraction (ME) attacks.<n>Existing defenses suffer from a critical ''Euclidean bias''<n>We propose GraphRP, a proactive defense framework that repurposes Model Reprogramming for security.
  arXiv  Detail & Related papers  (2026-08-11T23:20:15Z)
• Enabling Adversarial Robustness in AI Models through Kubeflow MLOps [0.21290878226779877]
  This paper presents security measures for AI models deployed in clusters.<n>The proposed architecture integrates Kubeflow-based MLOps to automatically detect adversarial attacks during the inference phase.<n>Experiments indicate that the deployed defense robustifies the model, significantly recovering accuracy relative to the degradation caused by the attack.
  arXiv  Detail & Related papers  (2026-05-14T12:45:36Z)
• GAMMAF: A Common Framework for Graph-Based Anomaly Monitoring Benchmarking in LLM Multi-Agent Systems [0.42970700836450487]
  We introduce Gammaf (Graph-based Anomaly Monitoring for LLM Multi-Agent systems Framework), an open-source benchmarking platform.<n>Gammaf is not a novel defense mechanism itself, but rather a comprehensive evaluation architecture designed to generate synthetic multi-agent interaction datasets.
  arXiv  Detail & Related papers  (2026-04-27T13:45:14Z)
• Transferable Physical-World Adversarial Patches Against Object Detection in Autonomous Driving [26.52089431845835]
  We propose AdvAD, a transfer-based physical attack against object detection in autonomous driving.<n>Instead of targeting a specific detector, AdvAD optimize adversarial patches over multiple detection models in a unified framework.<n>Experiments in both digital and real-world settings show that AdvAD consistently outperforms state-of-the-art (SOTA) attacks in performance and transferability.
  arXiv  Detail & Related papers  (2026-04-25T02:00:01Z)
• Boosting Robust AIGI Detection with LoRA-based Pairwise Training [55.076681464804636]
  Current AIGI detectors perform admirably on clean datasets, but their detection performance decreases when deployed "in the wild"<n>We propose a novel Lo-based Pairwise Training (RA) strategy designed specifically to achieve robust detection for AIGI under severe distortions.
  arXiv  Detail & Related papers  (2026-04-14T05:35:32Z)
• Model-agnostic Adversarial Attack and Defense for Vision-Language-Action Models [25.45513133247862]
  Vision-Language-Action (VLA) models have achieved revolutionary progress in robot learning.<n>Despite this progress, their adversarial robustness remains underexplored.<n>We propose both adversarial patch attack and corresponding defense strategies for VLA models.
  arXiv  Detail & Related papers  (2025-10-15T07:42:44Z)
• MISLEADER: Defending against Model Extraction with Ensembles of Distilled Models [56.09354775405601]
  Model extraction attacks aim to replicate the functionality of a black-box model through query access.<n>Most existing defenses presume that attacker queries have out-of-distribution (OOD) samples, enabling them to detect and disrupt suspicious inputs.<n>We propose MISLEADER, a novel defense strategy that does not rely on OOD assumptions.
  arXiv  Detail & Related papers  (2025-06-03T01:37:09Z)
• RADEP: A Resilient Adaptive Defense Framework Against Model Extraction Attacks [6.6680585862156105]
  We introduce a Resilient Adaptive Defense Framework for Model Extraction Attack Protection (RADEP)<n>RADEP employs progressive adversarial training to enhance model resilience against extraction attempts.<n> Ownership verification is enforced through embedded watermarking and backdoor triggers.
  arXiv  Detail & Related papers  (2025-05-25T23:28:05Z)
• Adversarial Training for Defense Against Label Poisoning Attacks [53.893792844055106]
  Label poisoning attacks pose significant risks to machine learning models.<n>We propose a novel adversarial training defense strategy based on support vector machines (SVMs) to counter these threats.<n>Our approach accommodates various model architectures and employs a projected gradient descent algorithm with kernel SVMs for adversarial training.
  arXiv  Detail & Related papers  (2025-02-24T13:03:19Z)
• Adversarial Robustness through Dynamic Ensemble Learning [0.0]
  Adversarial attacks pose a significant threat to the reliability of pre-trained language models (PLMs)<n>This paper presents Adversarial Robustness through Dynamic Ensemble Learning (ARDEL), a novel scheme designed to enhance the robustness of PLMs against such attacks.
  arXiv  Detail & Related papers  (2024-12-20T05:36:19Z)
• Robust Image Classification: Defensive Strategies against FGSM and PGD Adversarial Attacks [0.0]
  Adversarial attacks pose significant threats to the robustness of deep learning models in image classification. This paper explores and refines defense mechanisms against these attacks to enhance the resilience of neural networks.
  arXiv  Detail & Related papers  (2024-08-20T02:00:02Z)
• MirrorCheck: Efficient Adversarial Defense for Vision-Language Models [55.73581212134293]
  We propose a novel, yet elegantly simple approach for detecting adversarial samples in Vision-Language Models. Our method leverages Text-to-Image (T2I) models to generate images based on captions produced by target VLMs. Empirical evaluations conducted on different datasets validate the efficacy of our approach.
  arXiv  Detail & Related papers  (2024-06-13T15:55:04Z)
• The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks [90.52808174102157]
  In safety-critical applications such as medical imaging and autonomous driving, it is imperative to maintain both high adversarial robustness to protect against potential adversarial attacks. A notable knowledge gap remains concerning the uncertainty inherent in adversarially trained models. This study investigates the uncertainty of deep learning models by examining the performance of conformal prediction (CP) in the context of standard adversarial attacks.
  arXiv  Detail & Related papers  (2024-05-14T18:05:19Z)
• Embodied Active Defense: Leveraging Recurrent Feedback to Counter Adversarial Patches [37.317604316147985]
  The vulnerability of deep neural networks to adversarial patches has motivated numerous defense strategies for boosting model robustness. We develop Embodied Active Defense (EAD), a proactive defensive strategy that actively contextualizes environmental information to address misaligned adversarial patches in 3D real-world settings.
  arXiv  Detail & Related papers  (2024-03-31T03:02:35Z)
• Enhancing Object Detection Robustness: Detecting and Restoring Confidence in the Presence of Adversarial Patch Attacks [2.963101656293054]
  This study evaluates defense mechanisms for the YOLOv5 model against adversarial patches.<n>We tested several defenses, including Segment and Complete (SAC), Inpainting, and Latent Diffusion Models.<n>Results indicate that adversarial patches reduce average detection confidence by 22.06%.
  arXiv  Detail & Related papers  (2024-03-04T13:32:48Z)
• Learn from the Past: A Proxy Guided Adversarial Defense Framework with Self Distillation Regularization [53.04697800214848]
  Adversarial Training (AT) is pivotal in fortifying the robustness of deep learning models. AT methods, relying on direct iterative updates for target model's defense, frequently encounter obstacles such as unstable training and catastrophic overfitting. We present a general proxy guided defense framework, LAST' (bf Learn from the Pbf ast)
  arXiv  Detail & Related papers  (2023-10-19T13:13:41Z)
• On the Real-World Adversarial Robustness of Real-Time Semantic Segmentation Models for Autonomous Driving [59.33715889581687]
  The existence of real-world adversarial examples (commonly in the form of patches) poses a serious threat for the use of deep learning models in safety-critical computer vision tasks. This paper presents an evaluation of the robustness of semantic segmentation models when attacked with different types of adversarial patches. A novel loss function is proposed to improve the capabilities of attackers in inducing a misclassification of pixels.
  arXiv  Detail & Related papers  (2022-01-05T22:33:43Z)
• Model-Agnostic Meta-Attack: Towards Reliable Evaluation of Adversarial Robustness [53.094682754683255]
  We propose a Model-Agnostic Meta-Attack (MAMA) approach to discover stronger attack algorithms automatically. Our method learns the in adversarial attacks parameterized by a recurrent neural network. We develop a model-agnostic training algorithm to improve the ability of the learned when attacking unseen defenses.
  arXiv  Detail & Related papers  (2021-10-13T13:54:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.