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Defense against adversarial malware obfuscation attacks using explainable artificial intelligence. [PDF]
This work analyses adversarial attacks against malware detectors as well as strategies of detection and defense against such attacks. With the goal to improve already existing detection methods and adapt them for use with any feature vectors, this work ...
Kasperavičius, Liudas,
core
DIFFender: Diffusion-Based Adversarial Defense against Patch Attacks [PDF]
Adversarial attacks, particularly patch attacks, pose significant threats to the robustness and reliability of deep learning models. Developing reliable defenses against patch attacks is crucial for real-world applications.
Ruan, Shouwei +6 more
core +1 more source
This study aims to effectively defend against complex and diverse adversarial attacks, enhance adversarial defense performance in the field of textual adversarial examples, and address existing issues in current defense methods such as models&rsquo ...
Chengcong Wu, Lei Che, Yan Hou
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Robust width: A lightweight and certifiable adversarial defense [PDF]
Deep neural networks are vulnerable to so-called adversarial examples: inputs which are intentionally constructed to cause the model to make incorrect predictions or classifications.
Goossens, Bart, Peck, Jonathan
core
Open-Set Adversarial Defense with Clean-Adversarial Mutual Learning [PDF]
International Journal of Computer Vision, 2022 Accepted by International Journal of Computer Vision (IJCV) 2022. Code will be available at https://github.com/rshaojimmy/ECCV2020-OSAD.
Vishal M Patel, Pong C Yuen, Rui Shao
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Adversarial Attacks and Defenses on Graphs
ACM SIGKDD Explorations Newsletter, 2021Deep neural networks (DNNs) have achieved significant performance in various tasks. However, recent studies have shown that DNNs can be easily fooled by small perturbation on the input, called adversarial attacks.
Wei Jin 0009 +6 more
openaire +1 more source
Sinkhorn Adversarial Attack and Defense
IEEE Transactions on Image Processing, 2022Adversarial attacks have been extensively investigated in the recent past. Quite interestingly, a majority of these attacks primarily work in the lp space. In this work, we propose a novel approach for generating adversarial samples using Wasserstein distance.
openaire +2 more sources
Robust Defense Against Adversarial Attacks with Defensive Preprocessing and Adversarial Training
2025 IEEE International Conference on Consumer Electronics (ICCE)Deep learning technologies have rapidly advanced, but concerns about their security and vulnerability to threats have emerged. Adversarial attacks, using carefully crafted perturbations, exploit these weaknesses, posing serious risks. This study introduces an integrated defensive preprocessing and adversarial training pipeline as a robust defense ...
Chih-Yang Lin +4 more
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Adversarial Attacks and Defenses
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020Deep neural networks (DNN) have achieved unprecedented success in numerous machine learning tasks in various domains. However, the existence of adversarial examples leaves us a big hesitation when applying DNN models on safety-critical tasks such as autonomous vehicles and malware detection.
Han Xu 0002 +3 more
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DeepRobust: a Platform for Adversarial Attacks and Defenses
Proceedings of the AAAI Conference on Artificial Intelligence, 2021DeepRobust is a PyTorch platform for generating adversarial examples and building robust machine learning models for different data domains. Users can easily evaluate the attack performance against different defense methods with DeepRobust and get performance analyzing visualization.
Yaxin Li 0001 +3 more
openaire +2 more sources