Zobrazeno 1 - 10
of 110
pro vyhledávání: '"Kang, Xiangui"'
Adversarial examples have shown a powerful ability to make a well-trained model misclassified. Current mainstream adversarial attack methods only consider one of the distortions among $L_0$-norm, $L_2$-norm, and $L_\infty$-norm. $L_0$-norm based meth
Externí odkaz:
http://arxiv.org/abs/2407.03115
Existing face forgery detection usually follows the paradigm of training models in a single domain, which leads to limited generalization capacity when unseen scenarios and unknown attacks occur. In this paper, we elaborately investigate the generali
Externí odkaz:
http://arxiv.org/abs/2406.20078
With the rapid progress of generative models, the current challenge in face forgery detection is how to effectively detect realistic manipulated faces from different unseen domains. Though previous studies show that pre-trained Vision Transformer (Vi
Externí odkaz:
http://arxiv.org/abs/2309.11092
Face forgery detection is essential in combating malicious digital face attacks. Previous methods mainly rely on prior expert knowledge to capture specific forgery clues, such as noise patterns, blending boundaries, and frequency artifacts. However,
Externí odkaz:
http://arxiv.org/abs/2304.12489
A great challenge to steganography has arisen with the wide application of steganalysis methods based on convolutional neural networks (CNNs). To this end, embedding cost learning frameworks based on generative adversarial networks (GANs) have been p
Externí odkaz:
http://arxiv.org/abs/2107.13151
In order to promote the rapid development of image steganalysis technology, in this paper, we construct and release a multivariable large-scale image steganalysis dataset called IStego100K. It contains 208,104 images with the same size of 1024*1024.
Externí odkaz:
http://arxiv.org/abs/1911.05542
Publikováno v:
In Neural Networks June 2023 163:219-232
With the recent development of deep learning on steganalysis, embedding secret information into digital images faces great challenges. In this paper, a secure steganography algorithm by using adversarial training is proposed. The architecture contain
Externí odkaz:
http://arxiv.org/abs/1804.07939
Different from the conventional deep learning work based on an images content in computer vision, deep steganalysis is an art to detect the secret information embedded in an image via deep learning, pose challenge of detection weak information invisi
Externí odkaz:
http://arxiv.org/abs/1711.09335
Publikováno v:
In Computers & Security January 2022 112