Results 41 to 50 of about 5,380,268 (331)
AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive Learning [PDF]
ACM Multimedia, 2023 Multimodal contrastive learning aims to train a general-purpose feature extractor, such as CLIP, on vast amounts of raw, unlabeled paired image-text data.
Ziqi Zhou +5 more
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Adversarial examples for models of code [PDF]
Proceedings of the ACM on Programming Languages, 2020 Neural models of code have shown impressive results when performing tasks such as predicting method names and identifying certain kinds of bugs. We show that these models are vulnerable to adversarial examples , and introduce a novel approach for attacking trained models of code using ...
Noam Yefet, Uri Alon 0002, Eran Yahav
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A Robust Adversarial Example Attack Based on Video Augmentation
Applied Sciences, 2023 Despite the success of learning-based systems, recent studies have highlighted video adversarial examples as a ubiquitous threat to state-of-the-art video classification systems.
Mingyong Yin +3 more
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Adversarial Examples Generation Method Based on Image Color Random Transformation [PDF]
Jisuanji kexue, 2023 Although deep neural networks(DNNs) have good performance in most classification tasks,they are vulnerable to adversarial examples,making the security of DNNs questionable.Research designs to generate strongly aggressive adversarial examples can help ...
BAI Zhixu, WANG Hengjun, GUO Kexiang
doaj +1 more source
Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks [PDF]
Computer Vision and Pattern Recognition, 2019 Deep neural networks are vulnerable to adversarial examples, which can mislead classifiers by adding imperceptible perturbations. An intriguing property of adversarial examples is their good transferability, making black-box attacks feasible in real ...
Yinpeng Dong +3 more
semanticscholar +1 more source
Semantic Adversarial Examples [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018 Deep neural networks are known to be vulnerable to adversarial examples, i.e., images that are maliciously perturbed to fool the model. Generating adversarial examples has been mostly limited to finding small perturbations that maximize the model prediction error.
Hossein Hosseini, Radha Poovendran
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Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks [PDF]
Network and Distributed System Security Symposium, 2017 Although deep neural networks (DNNs) have achieved great success in many tasks, they can often be fooled by \emph{adversarial examples} that are generated by adding small but purposeful distortions to natural examples.
Weilin Xu, David Evans, Yanjun Qi
semanticscholar +1 more source
AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion Models [PDF]
European Conference on Computer Vision, 2023 Unrestricted adversarial attacks present a serious threat to deep learning models and adversarial defense techniques. They pose severe security problems for deep learning applications because they can effectively bypass defense mechanisms.
Xuelong Dai, Kaisheng Liang, Bin Xiao
semanticscholar +1 more source
CoRR, 2020 Appears in: Advances in Neural Information Processing Systems 33 (NeurIPS 2020)
Avishek Joey Bose +6 more
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Evaluation of Model Quantization Method on Vitis-AI for Mitigating Adversarial Examples
IEEE Access, 2023 Adversarial examples (AEs) are typical model evasion attacks and security threats in deep neural networks (DNNs). One of the countermeasures is adversarial training (AT), and it trains DNNs by using a training dataset containing AEs to achieve robustness
Yuta Fukuda +2 more
doaj +1 more source