Clustering Approach for Detecting Multiple Types of Adversarial Examples
Sensors, 2022 With intentional feature perturbations to a deep learning model, the adversary generates an adversarial example to deceive the deep learning model.
Seok-Hwan Choi +3 more
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DeepMal: maliciousness-Preserving adversarial instruction learning against static malware detection
Cybersecurity, 2021 Outside the explosive successful applications of deep learning (DL) in natural language processing, computer vision, and information retrieval, there have been numerous Deep Neural Networks (DNNs) based alternatives for common security-related scenarios ...
Chun Yang +6 more
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Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data [PDF]
Knowledge Discovery and Data Mining, 2021 Treatment effect estimation from observational data is a critical research topic across many domains. The foremost challenge in treatment effect estimation is how to capture hidden confounders.
Zhixuan Chu, S. Rathbun, Sheng Li
semanticscholar +1 more source
Not all adversarial examples require a complex defense : identifying over-optimized adversarial examples with IQR-based logit thresholding [PDF]
, 2019 Detecting adversarial examples currently stands as one of the biggest challenges in the field of deep learning. Adversarial attacks, which produce adversarial examples, increase the prediction likelihood of a target class for a particular data point ...
De Neve, Wesley +2 more
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VITAL: VIsual Tracking via Adversarial Learning [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 The tracking-by-detection framework consists of two stages, i.e., drawing samples around the target object in the first stage and classifying each sample as the target object or as background in the second stage.
Yibing Song +8 more
semanticscholar +1 more source
Adversarial Discriminative Domain Adaptation [PDF]
Computer Vision and Pattern Recognition, 2017 Adversarial learning methods are a promising approach to training robust deep networks, and can generate complex samples across diverse domains. They can also improve recognition despite the presence of domain shift or dataset bias: recent adversarial ...
Eric Tzeng +3 more
semanticscholar +1 more source
Anomaly-Based Intrusion on IoT Networks Using AIGAN-a Generative Adversarial Network
IEEE Access, 2023 Adversarial attacks have threatened the credibility of machine learning models and cast doubts over the integrity of data. The attacks have created much harm in the fields of computer vision, and natural language processing.
Zhipeng Liu +5 more
doaj +1 more source
Adversarial Attacks and Defenses in Deep Learning
Engineering, 2020 With the rapid developments of artificial intelligence (AI) and deep learning (DL) techniques, it is critical to ensure the security and robustness of the deployed algorithms.
Kui Ren +3 more
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A Survey on Efficient Methods for Adversarial Robustness
IEEE Access, 2022 Deep learning has revolutionized computer vision with phenomenal success and widespread applications. Despite impressive results in complex problems, neural networks are susceptible to adversarial attacks: small and imperceptible changes in input space ...
Awais Muhammad, Sung-Ho Bae
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A Study of Adversarial Attacks and Detection on Deep Learning-Based Plant Disease Identification
Applied Sciences, 2021 Transfer learning using pre-trained deep neural networks (DNNs) has been widely used for plant disease identification recently. However, pre-trained DNNs are susceptible to adversarial attacks which generate adversarial samples causing DNN models to make
Zhirui Luo, Qingqing Li, Jun Zheng
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