Results 31 to 40 of about 4,313 (262)

Scaling provable adversarial defenses

CoRR, 2018
Recent work has developed methods for learning deep network classifiers that are provably robust to norm-bounded adversarial perturbation; however, these methods are currently only possible for relatively small feedforward networks. In this paper, in an effort to scale these approaches to substantially larger models, we extend previous work in three ...
Eric Wong 0001, Frank R. Schmidt, Jan Hendrik Metzen, J. Zico Kolter   +3 more
openaire   +3 more sources

Deepfake Cross-Model Defense Method Based on Generative Adversarial Network [PDF]

Jisuanji gongcheng
To 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
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, Tianhang Zheng, Zhan Qin, Xue Liu   +3 more
doaj   +1 more source

Text Adversarial Purification as Defense against Adversarial Attacks

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023
Accepted by ACL2023 main ...
Linyang Li, Demin Song, Xipeng Qiu
openaire   +2 more sources

Developing a Robust Defensive System against Adversarial Examples Using Generative Adversarial Networks

Big Data and Cognitive Computing, 2020
In this work, we propose a novel defense system against adversarial examples leveraging the unique power of Generative Adversarial Networks (GANs) to generate new adversarial examples for model retraining. To do so, we develop an automated pipeline using
Shayan Taheri, Aminollah Khormali, Milad Salem, Jiann-Shiun Yuan   +3 more
doaj   +1 more source

Leveraging linear mapping for model-agnostic adversarial defense

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, Yajing Liu, Nathaniel Blanchard, Michael Kirby, Michael Kirby, Chris Peterson   +5 more
doaj   +1 more source

Adversarial Robustness Enhancement of UAV-Oriented Automatic Image Recognition Based on Deep Ensemble Models

Remote Sensing, 2023
Deep neural networks (DNNs) have been widely utilized in automatic visual navigation and recognition on modern unmanned aerial vehicles (UAVs), achieving state-of-the-art performances.
Zihao Lu, Hao Sun, Yanjie Xu
doaj   +1 more source

An Empirical Review of Adversarial Defenses

CoRR, 2020
19 pages, 8 Figures, Report Reviewed by Vivek ...
openaire   +2 more sources

Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses

CoRR, 2020
Advances in the development of adversarial attacks have been fundamental to the progress of adversarial defense research. Efficient and effective attacks are crucial for reliable evaluation of defenses, and also for developing robust models. Adversarial attacks are often generated by maximizing standard losses such as the cross-entropy loss or maximum ...
Gaurang Sriramanan, Sravanti Addepalli, Arya Baburaj, Venkatesh Babu R.   +3 more
openaire   +3 more sources

Multi-Line Defense Against Windows Adversarial Malware by Using Windows PE Information

IEEE Access
Deep learning has recently been in the spotlight among malware detection researchers in the sense that its training-based robust decision process can lead to efficient and effective malware detection.
Hannah Ho, Jun-Won Ho, Sungjin Ho
doaj   +1 more source