Nenghai Yu

Reversible Data Hiding in Encrypted Images by Reversible Image Transformation

With the popularity of outsourcing data to the cloud, it is vital to protect the privacy of data and enable the cloud server to easily manage the data at the same time. Under such demands, reversible data hiding in encrypted images (RDH-EI) attracts more and more researchers attention. In this paper, we propose a novel framework for RDH-EI based on reversible image transformation (RIT). Different from all previous encryption-based frameworks, in which the ciphertexts may attract the notation of the curious cloud, RIT-based framework allows the user to transform the content of original image into the content of another target image with the same size. The transformed image, that looks like the target image, is used as the “encrypted image,” and is outsourced to the cloud. Therefore, the cloud server can easily embed data into the “encrypted image” by any RDH methods for plaintext images. And thus a client-free scheme for RDH-EI can be realized, that is, the data-embedding process executed by the cloud server is irrelevant with the processes of both encryption and decryption. Two RDH methods, including traditional RDH scheme and unified embedding and scrambling scheme, are adopted to embed watermark in the encrypted image, which can satisfy different needs on image quality and large embedding capacity, respectively.

Comments on “Steganography Using Reversible Texture Synthesis”

Message hiding in texture image synthesis is a novel steganography approach by which we resample a smaller texture image and synthesize a new texture image with a similar local appearance and an arbitrary size. However, the mirror operation over the image boundary is flawed and is easy to attack. We propose an attacking method on this steganography, which can not only detect the stego-images but can also extract the hidden messages.

MoVieUp: Automatic Mobile Video Mashup

With the proliferation of mobile devices, people are taking videos of the same events anytime and anywhere. Even though these crowdsourced videos are uploaded to the cloud and shared, the viewing experience is very limited due to monotonous viewing, visual redundancy, and bad audio-video quality. In this paper, we present a fully automatic mobile video mashup system that works in the cloud to combine recordings captured by multiple devices from different view angles and at different time slots into a single yet enriched and professional looking video-audio stream. We summarize a set of computational filming principles for multicamera settings from a formal focus study. Based on these principles, given a set of recordings of the same event, our system is able to synchronize these recordings with audio fingerprints, assess audio and video quality, detect video cut points, and generate video and audio mashups. The audio mashup is the maximization of audio quality under the less switching principle, while the video mashup is formalized as maximizing video quality and content diversity, constrained by the summarized filming principles. Our system is different from any existing work in this field in three ways: 1) our system is fully automatic; 2) the system incorporates a set of computational domain-specific filming principles summarized from a formal focus study; and 3) in addition to video, we also consider audio mashup that is a key factor of user experience (UX) yet often overlooked in existing research. Evaluations show that our system achieves performance results that are superior to state-of-the-art video mashup techniques, thus providing a better UX.

Image camouflage by reversible image transformation

Reduce the information for recording block indexes by non-uniform clustering algorithm and thus improve the quality of camouflage image by setting small block size.Modify the previous color transformation to be reversible. So the secret image can be transformed to a freely-selected target image, getting a camouflage image used as the camouflage of secret image with good visual quality, and the secret image can be restored without any loss.Because of the reversibility, we can further realize multi-round transformation, that is, transform the camouflage image to another target image. A new reversible image transformation technique is proposed, which not only improves the visual quality of the camouflage image created by transforming a secret image to a freely-selected target image, but also can restore the secret image without any loss. Effective clustering algorithm is utilized to reduce the information for recording block indexes that is vital for restoring the secret image. Therefore, the transformation can be made between the blocks with relatively small size, thus greatly improving the visual quality of the camouflage image. The root mean square error of camouflage image was reduced by 6 in most cases compared with the previous method. Since the proposed technique is reversible, we can further realize two-round transformation by transforming the camouflage image to another target image and thus hide two images into only one.

Defining cost functions for adaptive steganography at the microscale

In the framework of minimizing embedding distortion steganography, the definition of cost function almost determines the security of the method. Generally speaking, texture areas would be assigned low cost, while smooth areas with high cost. However, the prior methods are still not precise enough to capture image details. In this paper, we present a novel scheme of defining cost function for adaptive steganography at the microscale. The proposed scheme is designed by using a “microscope” to highlight fine details in an image so that distortion definition can be more refined. Experiments show that by adopting our scheme, the current steganographic methods (WOW, UNIWARD, HILL) will achieve better performances on resisting the state-of-the-art steganalysis.

Reversible data hiding in JPEG image based on DCT frequency and block selection

Abstract The joint photographic experts group (JPEG) is the most popular format of digital image. In this paper, a novel reversible data hiding scheme based on JPEG image is proposed. In JPEG image, quantified DCT (discrete cosine transform) coefficients with different frequencies will yield different capacities and embedding distortions. To reduce the total distortion for the marked image, we select coefficients from frequencies yielding less distortions for embedding, and then an advanced block selection strategy is applied to always modify the block yielding less simulated distortion firstly until the given payloads are completely embedded. Experimental results show that the proposed method can keep good visual quality with small bitstream expansion.

Reversible data hiding for 3D mesh models with three-dimensional prediction-error histogram modification

This study proposes a reversible data hiding (RDH) algorithm for 3D mesh models based on the optimal three-dimensional prediction-error histogram (PEH) modification with recursive construction coding (RCC). Firstly, we design a double-layered prediction scheme to divide all the vertices of 3D mesh model into the “embedded” set and the “referenced” set, according to the odd-even property of indices into the vertex list. Thanks to the geometrical similarity among neighboring vertices, we obtain the prediction errors (PEs) with a sharp histogram. Then we combine every three adjacent PEs into one prediction-error triplet (PET) and construct the three-dimensional PEH with smaller entropy than one-dimensional PEH by utilizing the correlation among PEs. Next we project the three-dimensional PEH into one-dimensional space for scalar PET sequence which is suitable for using RCC. And also, we define the distortion metrics for 3D mesh models, by which we can estimate the optimal probability transition matrix (OTPM) indicating the optimal PEH modification manner. After that, we modify the PET sequence and embed data by RCC according to OTPM. The experimental results show that our method is superior to two state-of-the-art spatial-domain RDH algorithms for 3D mesh models a lot.

Defining Joint Distortion for JPEG Steganography

Recent studies have shown that the non-additive distortion model of Decomposing Joint Distortion (

Adversarial Examples Against Deep Neural Network based Steganalysis

DeJoin

Side Channel Steganalysis: When Behavior is Considered in Steganographer Detection

) can work well for spatial image steganography by defining joint distortion with the principle of Synchronizing Modification Directions (SMD). However, no principles have yet produced to instruct the definition of joint distortion for JPEG steganography. Experimental results indicate that SMD can not be directly used for JPEG images, which means that simply pursuing modification directions clustered does not help improve the steganographic security. In this paper, we inspect the embedding change from the spatial domain and propose a principle of Block Boundary Continuity (BBC) for defining JPEG joint distortion, which aims to restrain blocking artifacts caused by inter-block adjacent modifications and thus effectively preserve the spatial continuity at block boundaries. According to BBC, whether inter-block adjacent modifications should be synchronized or desynchronized is related to the DCT mode and the adjacent direction of inter-block coefficients (horizontal or vertical). When built into