Results 41 to 50 of about 5,384,791 (287)
Survey on adversarial attacks and defense of face forgery and detection
网络与信息安全学报, 2023 Face forgery and detection has become a research hotspot.Face forgery methods can produce fake face images and videos.Some malicious videos, often targeting celebrities, are widely circulated on social networks, damaging the reputation of victims and ...
Shiyu HUANG, Feng YE, Tianqiang HUANG, Wei LI, Liqing HUANG, Haifeng LUO
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Detecting Overfitting via Adversarial Examples
CoRR, 2019 17 ...
Roman Werpachowski +2 more
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SafetyNet: Detecting and Rejecting Adversarial Examples Robustly [PDF]
2017 IEEE International Conference on Computer Vision (ICCV), 2017 We describe a method to produce a network where current methods such as DeepFool have great difficulty producing adversarial samples. Our construction suggests some insights into how deep networks work. We provide a reasonable analyses that our construction is difficult to defeat, and show experimentally that our method is hard to defeat with both Type
Jiajun Lu +2 more
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Object Detection Adversarial Attack for Infrared Imagery in Remote Sensing [PDF]
Hangkong bingqi, 2022 Aiming at the problems of poor effect of existing adversarial attack for object detection algorithms on small-scale target attack, a large number of meaningless disturbances in adversarial samples and low disturbance genera-tion efficiency, taking ...
Qi Jiahao, Zhang Yu, Wan Pengcheng, Li Yuanzhe, Liu Xingyue, Yao Aihuan, Zhong Ping
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ReabsNet: Detecting and Revising Adversarial Examples
CoRR, 2017 Though deep neural network has hit a huge success in recent studies and applica- tions, it still remains vulnerable to adversarial perturbations which are imperceptible to humans. To address this problem, we propose a novel network called ReabsNet to achieve high classification accuracy in the face of various attacks.
Jiefeng Chen 0001 +4 more
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Detecting Adversarial Examples Using Surrogate Models
Machine Learning and Knowledge Extraction, 2023 Deep Learning has enabled significant progress towards more accurate predictions and is increasingly integrated into our everyday lives in real-world applications; this is true especially for Convolutional Neural Networks (CNNs) in the field of image analysis.
Borna Feldsar +2 more
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Multiple Classifier Systems in Adversarial Environments: "Challenges and Solutions" [PDF]
, 2009 Pattern recognition methods offer technological background for a variety of applications in a modern information society. They are however undermined by several kinds of "adversarial" misuses like email and web spam, attacks to computer networks, etc.
Gargiulo, Francesco
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Image Classification Adversarial Example Defense Method Based on Conditional Diffusion Model [PDF]
Jisuanji gongchengDeep-learning models have achieved impressive results in fields such as image classification; however, they remain vulnerable to interference and threats from adversarial examples.
CHEN Zimin, GUAN Zhitao
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Training Ensembles to Detect Adversarial Examples
CoRR, 2017 We propose a new ensemble method for detecting and classifying adversarial examples generated by state-of-the-art attacks, including DeepFool and C&W. Our method works by training the members of an ensemble to have low classification error on random benign examples while simultaneously minimizing agreement on examples outside the training ...
Alexander Bagnall +2 more
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Meta-Learning Adversarial Example Detection Model Based on Fused Features [PDF]
Jisuanji kexue yu tansuoDeep learning models are vulnerable to adversarial attacks, rendering adversarial example detection critical. Existing detection methods rely on large amounts of labeled data for training, while new adversarial examples are generated faster than data ...
JIANG Zhangtao, LI Xin, XUE Di, WANG Xiaoyu
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