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Key distribution is the foundation for protecting users' privacy and communication security in cloud environment. Information hiding is an effective manner to hide the transmission behavior of secret information such as keys, and thus it makes the secure key distribution possible. However, the traditional information hiding systems usually embed the secret information by modifying the carrier, which inevitably leaves modification traces on the carrier. Thus, they cannot resist the detection of the steganalysis algorithm effectively. To avoid this issue, the coverless information hiding technique has been proposed accordingly, in which the original images of which features can express the secret information are directly used as stegoimages. Since the existing coverless information hiding methods use the low-level handcrafted image features to express secret information, it is hard for them to realize desirable robustness against common image attacks. Moreover, their hiding capacity is limited. To conquer these problems, we design a novel robust image coverless information hiding system using Faster Region-based Convolutional Neural Networks (Faster-RCNN). We employ Faster-RCNN to detect and locate objects in images and utilize the labels of these objects to express secret information. Since the original images without any modification are used as stego-images, the proposed method can effectively resist steganalysis and will not cause attackers' suspicion. The experimental results demonstrate that the proposed system has higher performance in terms of robustness and capacity compared to the typical coverless information hiding methods.

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As one of the important techniques for protecting the copyrights of digital images, content-based image copy detection has attracted a lot of attention in the past few decades. The traditional content-based copy detection methods usually extract local hand-crafted features and then quantize these features to visual words by the bag-of-visual-words (BOW) model to build an inverted index file for rapid image matching. Recently, deep learning features, such as the features derived from convolutional neural networks (CNN), have been proven to outperform the hand-crafted features in many applications of computer vision. However, it is not feasible to directly apply the existing global CNN features for copy detection, since they are usually sensitive to partial content-discarded attacks, such as copping and occlusion. Thus, we propose a local CNN feature-based image copy detection method with contextual hash embedding. We first extract the local CNN features from images and then quantize them to visual words to construct an index file. Then, as the BOW quantization process decreases the discriminability of these features to some extent, a contextual hash sequence is captured from a relatively large region surrounding each CNN feature and then is embedded into the index file to improve the feature’s discriminability. Extensive experimental results demonstrate that the proposed method achieves a superior performance compared to the related works in the copy detection task.

Digital image watermarking is one of the effective schemes to protect the copyrights of still images. However, the existing watermarking schemes are still not robust enough to the common geometric transformation attacks such as arbitrary rotation, scaling and shifting with desirable hiding capacity. To address this issue, we propose a robust watermarking scheme based on geometric correction codes (GCCs). In this scheme, the watermark and pre‐set GCCs are combined and embedded into a cover image to obtain the watermarked image. At the stage of watermark extraction, the watermarked image, under a variety of geometric transformation attacks, can be geometrically corrected by minimising the difference between the extracted and the original GCCs, then the watermark is extracted from the watermarked image. The experiments demonstrate that, compared to the typical watermarking schemes, the proposed scheme achieves much higher robustness to the common geometric transformation attacks and comparable invisibility with the same embedding capacity.