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Towards Accuracy-Fairness Paradox: Adversarial Example-based Data Augmentation for Visual Debiasing
ACM Multimedia, 2020Machine learning fairness concerns about the biases towards certain protected or sensitive group of people when addressing the target tasks. This paper studies the debiasing problem in the context of image classification tasks.
Yi Zhang, Jitao Sang
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Review on Image Processing Based Adversarial Example Defenses in Computer Vision
2020 IEEE 6th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS), 2020Recent research works showed that deep neural networks are vulnerable to adversarial examples, which are usually maliciously created by carefully adding deliberate and imperceptible perturbations to examples. Several states of the art defense methods are
Meikang Qiu, Han Qiu
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POSTER: Detecting Audio Adversarial Example through Audio Modification
Conference on Computer and Communications Security, 2019Deep neural networks (DNNs) perform well in the fields of image recognition, speech recognition, pattern analysis, and intrusion detection. However, DNNs are vulnerable to adversarial examples that add a small amount of noise to the original samples ...
Hyun Kwon, H. Yoon, Ki-Woong Park
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New algorithm to generate the adversarial example of image
, 2020This paper focuses on the algorithm to generate adversarial example of image. Firstly a new memristive chaotic mapping was proposed. Then two chaotic sequences based on the constructed memristive chaotic mapping are adopted to design the algorithm to ...
Bo Wang, F. Zou, X. W. Liu
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Adversarial Examples in Arabic
2019 International Conference on Computational Science and Computational Intelligence (CSCI), 2019Several studies have shown that deep neural networks (DNNs) are vulnerable to adversarial examples - perturbed inputs that cause DNN-based models to produce incorrect outputs. A variety of adversarial attacks have been proposed in the domains of computer vision and natural language processing (NLP); however, most attacks in the NLP domain have been ...
Basemah Alshemali, Jugal Kalita
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Revealing Perceptual Proxies with Adversarial Examples
IEEE Transactions on Visualization and Computer Graphics, 2021Data visualizations convert numbers into visual marks so that our visual system can extract data from an image instead of raw numbers. Clearly, the visual system does not compute these values as a computer would, as an arithmetic mean or a correlation. Instead, it extracts these patterns using perceptual proxies; heuristic shortcuts of the visual marks,
Brian D, Ondov +4 more
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Adversarial Examples for Hamming Space Search
IEEE Transactions on Cybernetics, 2020Due to its strong representation learning ability and its facilitation of joint learning for representation and hash codes, deep learning-to-hash has achieved promising results and is becoming increasingly popular for the large-scale approximate nearest neighbor search.
Erkun Yang +3 more
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Advops: Decoupling Adversarial Examples
Pattern Recognition, 2023Donghua Wang +3 more
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Adversarial example detection based on saliency map features
Applied intelligence (Boston), 2021Shen Wang, Yuxin Gong
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Rethinking Adversarial Examples
Traditionally, adversarial examples have been defined as imperceptible perturbations that fool deep neural networks. This thesis challenges this view by examining unrestricted adversarial examples – a broader class of manipulations that can compromise model security while preserving semantics.openaire +1 more source