Image Generation Network for Covert Transmission in Online Social Network

Online social networks have stimulated communications over the Internet more than ever, making it possible for secret message transmission over such noisy channels. In this paper, we propose a Coverless Image Steganography Network, called CIS-Net, that synthesizes a high-quality image directly conditioned on the secret message to transfer. CIS-Net is composed of four modules, namely, the Generation, Adversarial, Extraction, and Noise Module. The receiver can extract the hidden message without any loss even the images have been distorted by JPEG compression attacks. To disguise the behaviour of steganography, we collected images in the context of profile photos and stickers and train our network accordingly. As such, the generated images are more inclined to escape from malicious detection and attack. The distinctions from previous image steganography methods are majorly the robustness and losslessness against diverse attacks. Experiments over diverse public datasets have manifested the superior ability of anti-steganalysis.

No way to crop: On robust image crop localization

Previous image forensics schemes for crop detection are only limited on predicting whether an image has been cropped. This paper presents a novel scheme for image crop localization using robust watermarking. We further extend our scheme to detect tampering attack on the attacked image. We demonstrate that our scheme is the first to provide high-accuracy and robust image crop localization. Besides, the accuracy of tamper detection is comparable to many state-of-the-art methods.