Gradually Enhanced Adversarial Perturbations on Color Pixel Vectors for Image Steganography

Xinghong Qin, Bin Li, Shunquan Tan, Weixuan Tang, Jiwu Huang

Abstract: Compared to element-wise embedding, vector-wise embedding based on CPV (color pixel vector) shows its superiority in color image steganography. However, when working with an adversarial embedding scheme for introducing adversarial perturbations, its success rate of deceiving a target CNN (convolutional neural network) steganalyzer dramatically drops. In this paper, inspired by the I-FGSM (iterative fast gradient sign method), we present an effective steganography for color images. Specifically, after decomposing an image into several non-overlapped sub-images, we iteratively and gradually increase the possibilities of generating adversarial perturbations for the CPVs in each sub-image by changing their adversarial costs. The costs are incrementally adjusted with a small step so that their maximum relative variation is minimized. Leveraging a new designed cost adjustment criterion, more modification patterns of CPV can participate in producing effective adversarial perturbations. Extensive experiments demonstrate that the proposed method achieves a high success rate in deceiving the target CNN steganalyzer and stably defending against the detection of other non-target steganalytic schemes for color images.