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- Title
SharpenNet: Detecting Anti-Forensics USM Sharpening Adversarial Examples Based on ConvNeXt.
- Authors
Yu, Haozheng; Fan, Bing; Xu, Bing; Zhu, Xiaogang
- Abstract
Image sharpening detection, as a crucial branch of image forensics research, has attained a satisfactory level of performance with the assistance of deep learning. However, due to the nature of convolutional neural network (CNN) models, adversarial examples synthesized by generative adversarial networks (GANs) can easily attack existing forensics models. Therefore, deep learning-based forensics faces new challenges. In this paper, a novel architecture inspired by ConvNext is proposed to detect synthesized adversarial USM sharpening images. Through practical demonstration, our proposed technique achieves satisfying performance in recognizing adversarial samples that outperform previous sharpened image forensic systems. In addition, we have undertaken an ablation analysis of our suggested network topology and analyzed the efficacy of different enhancements.
- Subjects
DEEP learning; CONVOLUTIONAL neural networks; GENERATIVE adversarial networks; IMAGING systems
- Publication
Journal of Circuits, Systems & Computers, 2024, Vol 33, Issue 6, p1
- ISSN
0218-1266
- Publication type
Article
- DOI
10.1142/S0218126624300034