SharpenNet: Detecting Anti-Forensics USM Sharpening Adversarial Examples Based on ConvNeXt
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.
This paper was recommended by Regional Editor Takuro Sato.
