Results 11 to 20 of about 7,737 (292)
Understanding and Improving Ensemble Adversarial Defense [PDF]
Advances in Neural Information Processing Systems 36, 2023 The strategy of ensemble has become popular in adversarial defense, which trains multiple base classifiers to defend against adversarial attacks in a cooperative manner. Despite the empirical success, theoretical explanations on why an ensemble of adversarially trained classifiers is more robust than single ones remain unclear.
Deng, Yian, Mu, Tingting
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Adversarial anchor-guided feature refinement for adversarial defense
Image and Vision Computing, 2023 Adversarial training (AT), which is known as a robust training method for defending against adversarial examples, usually loses the performance of models for clean examples due to the feature distribution discrepancy between clean and adversarial.
Hakmin Lee, Yong Man Ro
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Deepfake Cross-Model Defense Method Based on Generative Adversarial Network [PDF]
Jisuanji gongchengTo reduce social risks caused by the abuse of deepfake technology, an active defense method against deep forgery based on a Generative Adversarial Network (GAN) is proposed. Adversarial samples are created by adding imperceptible perturbation to original
DAI Lei, CAO Lin, GUO Yanan, ZHANG Fan, DU Kangning
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Adversarial Backdoor Defense in CLIP
CoRRMultimodal contrastive pretraining, exemplified by models like CLIP, has been found to be vulnerable to backdoor attacks. While current backdoor defense methods primarily employ conventional data augmentation to create augmented samples aimed at feature alignment, these methods fail to capture the distinct features of backdoor samples, resulting in ...
Junhao Kuang +4 more
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A Mask-Based Adversarial Defense Scheme
Algorithms, 2022 Adversarial attacks hamper the functionality and accuracy of deep neural networks (DNNs) by meddling with subtle perturbations to their inputs. In this work, we propose a new mask-based adversarial defense scheme (MAD) for DNNs to mitigate the negative ...
Weizhen Xu +3 more
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Leveraging linear mapping for model-agnostic adversarial defense [PDF]
Frontiers in Computer Science, 2023 In the ever-evolving landscape of deep learning, novel designs of neural network architectures have been thought to drive progress by enhancing embedded representations.
Huma Jamil +5 more
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Defensive Dual Masking for Robust Adversarial Defense
Computational LinguisticsAbstract Adversarial defenses for textual data have gained considerable attention in recent years due to the increasing vulnerability of Natural Language Processing (NLP) models to adversarial attacks. These attacks exploit subtle perturbations in input text to deceive models, posing significant challenges to model robustness and ...
Wangli Yang +3 more
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LPF-Defense: 3D adversarial defense based on frequency analysis. [PDF]
PLoS One, 2023 The 3D point clouds are increasingly being used in various application including safety-critical fields. It has recently been demonstrated that deep neural networks can successfully process 3D point clouds. However, these deep networks can be misclassified via 3D adversarial attacks intentionality designed to perturb some point cloud’s features.
Naderi H +3 more
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Detection and Defense: Student-Teacher Network for Adversarial Robustness
IEEE AccessDefense against adversarial attacks is critical for the reliability and safety of deep neural networks (DNNs). Current state-of-the-art defense methods achieve significant robustness against adversarial attacks.
Kyoungchan Park, Pilsung Kang
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
Muzammal Naseer +4 more
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