Residual Steganography: Embedding Secret Data in Images using Residual Networks

Residual Steganography: Embedding Secret Data in Images using Residual Networks

연구 분야: Strategies

학회: 2023 6th International Conference on Information Systems and Computer Networks (ISCON)

All the residing image steganography methods depicts the problem of degradation, low capacity. In order to overcome this problem, we introduce an encoder decoder based residual network along with convolutional neural network to conceal one image into another. This paper presents a novel approach to image steganography in the residual domain. Traditional image steganography techniques typically involve embedding information directly into the image data, which can often lead to noticeable artifacts or degradation of the image quality. To evaluate the effectiveness of our approach, we conducted a series of experiments using a large dataset of natural images. Our results show that our approach is able to conceal a significant amount of secret data with minimal impact on the visual quality of the image. Moreover, our method is robust against various steganalysis techniques, making it suitable for secure communication applications. Overall, our proposed approach represents a promising direction for image steganography in the residual domain.