Recently, artificial intelligence (AI) algorithm has been extensively utilized as an optimization tool in digital watermarking. However, existing works seldom consider the reversibility of the embedding framework, thereby neglecting the protection of sensitive carriers. In this paper, we propose an AI-assisted reversible data hiding (RDH) method based on reinforcement learning. In our method, the Q-Learning algorithm is introduced to address the optimization problem of RDH, i.e., adaptive two-dimensional (2D) mapping generation, and it is designed to simulate pixel modifications in 2D space, employing a Markov decision process formulation within the reinforcement learning paradigm. The new reward function is given to evaluate the effectiveness of 2D mapping based on the estimated embedding capacity and the distortion-capacity ratio. To ensure reversibility, a mapping adjustment strategy is implemented to update the environmental states. Experimental results show that the proposed method outperforms conventional 2D RDH and demonstrates competitive performance compared to other state-of-the-art RDH methods.

With the rapid development of the metaverse, massive amounts of 3D data are created and outsourced in the cloud, and ciphertext policy attribute-based encryption (CP-ABE) is widely used in fine-grained access control to achieve secure outsourced data sharing. However, the prominent security risk is due to the fact that authorized data users may later become traitors and illegally redistribute the 3D models to the public. To protect the rights of the creator, a traitor tracing and access control method for encrypted 3D models is proposed using CP-ABE and fair watermark to meet the security needs in the metaverse. First, a commutative watermark/encryption method based on the orthogonal operation domain is designed, and the 3D model is encrypted by CP-ABE. Then, a fair watermark protocol protects the rights of the parties. Finally, the blockchain acts as a trusted third party and records the authentication information for traitor tracing. The experimental results demonstrate the feasibility and safety of the proposed method.

Generative text steganography has received considerable attention in the covert communication community for the benefit of sending secret messages without the need to modify carriers. Existing methods typically choose the next word when generating a stego-text based on conditional probability encoding of candidates, which may lead to generating inadequate words for the underlying secret message. How to generate a semantically controllable stego-text with a high capacity on secure embedding of a secret message is a main challenge. We address this challenge by proposing a new paradigm to generative text steganography that takes advantage of certain social media through apparently normal behaviors from the sender. In particular, we make use of the live commenting feature provided by public video sharing platforms (PVSPs), which allow viewers to make comments on video scenes that will fly on screens when the scenes are shown. We show that this feature can be used to construct a generative steganographic system. The sender generates at random a number of distracting words and a certain invertible matrix called W-dmatrix based on the total number of message words and distracting words. The sender then transforms a sequence of indexes of these words to a sequence, selects one or more videos with a sufficiently large number of total frames, and generates a comment on each frame in the sequence. The receiver extracts commented frame indexes, uses the shared stego-key to generate the same W-dmatrix as the sender, and obtains the secret message using the inverse of the matrix. The stego-key consists of a vocabulary generator and a W-dmatrix generator (WMG) based on pseudorandomly generated numbers. To generate comments on frames that conform to comments made by viewers, we devise a neural ResNet-LSTM model to generate a comment for an input image based on its content. Theoretical analysis shows that commented video frames (CVF) is covert, secure, efficient, and feasible to conceal any message of arbitrary length. We implement CVF and present evaluation results from multiple aspects that our work outperforms the existing stego-methods.

Reversible data hiding in encrypted domain (RDH-ED) can perform data encryption to fulfill the privacy protection of original media and embed additional data for covert communication or access control. However, current researches are focusing on the encrypted images, and little attention is paid to encrypted three-dimensional (3D) models. In this article, a high capacity separable RDH-ED method for encrypted 3D models is proposed based on octree spatial subdivision and multiple most significant bit (multi-MSB) prediction. Firstly, a 3D model is adaptively subdivided into non-overlapping subblocks by octree spatial subdivision, and the vertices in a subblock are classified into embedding set and reference set. To better utilize the spatial correlation of the two sets, the multi-MSB prediction error of the embedding set is used to embed the additional data, and the reference set is used to losslessly recover the embedded set. Then, the model is encrypted by a specified encrypted algorithm. At last, additional data is embedded into the reserved embedding room by multi-MSB substitution. Experimental results show that the proposed method can achieve a higher embedding capacity compared with the state-of-the-art methods, and guarantee the lossless recovery of the 3D model.

Malicious Deepfakes have led to a sharp conflict over distinguishing between genuine and forged faces. Although many countermeasures have been developed to detect Deepfakes ex-post, undoubtedly, passive forensics has not considered any preventive measures for the pristine face before foreseeable manipulations. To complete this forensics ecosystem, we thus put forward the proactive solution dubbed SepMark, which provides a unified framework for source tracing and Deepfake detection. SepMark originates from encoder-decoder-based deep watermarking but with two separable decoders. For the first time the deep separable watermarking, SepMark brings a new paradigm to the established study of deep watermarking, where a single encoder embeds one watermark elegantly, while two decoders can extract the watermark separately at different levels of robustness. The robust decoder termed Tracer that resists various distortions may have an overly high level of robustness, allowing the watermark to survive both before and after Deepfake. The semi-robust one termed Detector is selectively sensitive to malicious distortions, making the watermark disappear after Deepfake. Only SepMark comprising of Tracer and Detector can reliably trace the trusted source of the marked face and detect whether it has been altered since being marked; neither of the two alone can achieve this. Extensive experiments demonstrate the effectiveness of the proposed SepMark on typical Deepfakes, including face swapping, expression reenactment, and attribute editing. Code will be available at https://github.com/sh1newu/SepMark.

Abstract In this paper, we propose a human visual system (HVS) guided reversible data hiding method based on multiple histograms modification. The proposed method utilizes the texture features of an image to adaptively modify the pixels, for a lower HVS quality distortion. The HVS quality is taken as the optimization objective and a new expansion-bin-selection strategy is given to solve the optimization. For the same HVS quality distortion, the proposed method can embed more secret bits and give higher priority for the modifications in texture regions. Besides, a better optimization rule is proposed to accelerate the speed and then consider more available solutions. In this way, a trade-off between the embedding performance and time complexity can be achieved. Experimental results show that the proposed method can achieve a better HVS quality than the conventional methods.

We propose a continuous-variable quantum secret sharing scheme based on multi-ring discrete modulation, which we called MR-CVQSS. In this scheme, phase shift keying (PSK)-modulated coherent states are allowed to be further discretely modulated with different amplitudes, so that these modulated coherent states can be scattered on different amplitude rings in phase space. The advantage for this multi-ring structure is that the error probability of quantum detector for discriminating discretely-modulated coherent states (DMCSs) is more lower than that of original single-ring discretely modulated CVQSS, which is beneficial for improving the performance of whole CVQSS system. We derive the security bound for MR-CVQSS against both eavesdroppers and dishonest users, and the numerical simulation shows that the performance of MR-CVQSS in terms of both maximal transmission distance and maximal available number of users can be largely improved. Moreover, security analysis shows that the modulation variance of MR-CVQSS does not need to be very small, thereby improving the signal-to-noise ratio (SNR) of whole CVQSS system.

Quick response (QR) code is widely used for mobile payment to convey information. However, the traditional QR code has no semantic content and cannot be understood by human. To remedy this, the QR code beautification is designed to transform the black-white pattern into a semantic content, and make the code more appealing for commercial applications. In this paper, we propose an improved QR code beautification method based on mask pattern optimization. By selecting the optimal mask pattern, the proposed method can reduce the interference of the masking process on the QR code beautification and improve the visual quality of the QR code. The key issue lies in encoding the QR pattern into a given visual content as similar as possible while keeping the acceptable decoding accuracy. The experimental results show that the proposed method can generate a more appealing aesthetic QR code and preserve the high decoding rate for various devices.

Pairwise prediction-error expansion (pairwise PEE) is an efficient reversible data hiding technique based on two-dimensional (2D) histogram modification. However, without considering the image content, its embedding manner lacks adaptivity. In this paper, we propose the adaptive modification for multiple prediction-error histograms (PEHs). An iterative self-learning optimization algorithm is devised to adaptively generate the 2D mapping, based on the PEH and payload. A loss function is employed and the optimization can be solved linearly to reduce the time cost. To determine the appropriate expansion bins, we generate multiple 2D PEHs and establish multiple candidate mappings. The optimal combination of the adaptive 2D mappings is determined by using exhaustive searching. Our method is flexible as the 2D mapping generation has a broad dynamic range. The experimental results show that the proposed method can outperform the pairwise PEE and also provide a competitive performance compared with the other state-of-the-art 2D RDH methods.

By integrating the micro-sensor on RFID tags to obtain the environment information, the sensor-augmented RFID system greatly supports the applications that are sensitive to environment. To quickly collect the information from all tags, many researchers dedicate on well arranging tag replying orders to avoid the signal collisions. Compared to from all tags, collecting information from a part of tags (i.e., target tags) is more challenging because the collecting process is interfered by useless replying from non-target tags. The existing works of target tag information collection are designed for single reader systems. However, they cannot work efficiently in more common multi-reader scenarios, where each reader lacks knowledge of tag distribution among all readers. In this paper, we propose time-efficient protocols to collect target tag information in multi-reader systems. The high efficiency of our protocol is enabled by two novel designs. First, we develop a technique that quickly detects and silences non-target tags without a priori knowledge of which tags are in the readers' interrogation regions. Second, we design an allocation vector to efficiently arrange the replying order of only target tags. Different from previous bit-vector based approaches that make use of only singleton slots, our allocation vector approach also makes use of collision slots to speed up target tag information collection. We further propose an enhancement protocol which can reconcile the collision slots with higher probability and therefore collect information from more target tags simultaneously. The extensive simulation results demonstrate that our protocols significantly outperform the state-of-the-art protocols in terms of time-efficiency.

Among various techniques of reversible data hiding (RDH), histogram modification has been mostly investigated and used in practice. Recently, a new histogram modification technique, namely, multiple histograms modification (MHM) has been proposed and received the increasing attentions. By MHM, multiple histograms can be modified differently to achieve an excellent embedding performance. However, as the maximum modification on each pixel value is one, only one pair of bins in the prediction-error histogram (PEH) is allowed to be expanded for data embedding. This drawback leads to a low capacity and limits its extension for high capacity scenario. To remedy this, we extend the MHM scheme and propose an efficient solution for the high-capacity embedding. Specifically, instead of only selecting one pair of expansion bins in each PEH, in the proposed method, multiple pairs of bins are utilized for expansion in each PEH. Moreover, the embedding performance is optimized by an advisable expansion bin selection strategy. Consequently, the multiple PEHs are allowed to embed with different capacities according to the sharpness of distribution, i.e., the low-index PEH that consists of more smooth pixels is embedded with a larger capacity than the high-index one. Besides, a simplified parameter determination is developed to seek the optimal solution with a good computational efficiency. The experimental results show that the proposed method can achieve the high capacity and provide a better performance than some state-of-the-art RDH methods.

Pairwise prediction-error expansion (pairwise PEE) is a recent technique for the high-dimensional reversible data hiding. However, in the absence of adaptive embedding, its potential has not been fully exploited. In this paper, we propose the adaptive pixel pairing (APP) and the adaptive mapping selection for the enhancement of pairwise PEE. Our motivation is twofold: building a sharper 2D histogram and designing the effective 2D mapping for it. In APP, we consider to increase the similarity between pixels in a pair, by excluding the rough pixels from pairing and only putting the smooth pixels into pairs. In this way, the pixels in a pair have a larger possibility of being equal, and thus the resulted 2D prediction-error histogram (PEH) has lower entropy. Next, the adaptive mapping selection mechanism is introduced to properly determine the optimal modification, based on “whether it fits for the resulted PEH” rather than heuristic experience. The experimental results show that the proposed method has a significant improvement over the pairwise PEE.

The latest research progress of the theory of compressed sensing (CS) over graphs makes it possible that the advantage of CS can be utilized by data ferries to gather data in WSNs. In this paper, we leverage the non-uniform distribution of the sensing data field to significantly reduce the required number of data ferries, yet ensuring the recovered data quality. Specially, we propose an intelligent compressive data gathering scheme consisting of an efficient stopping criterion and a novel learning strategy. The proposed stopping criterion is based only on the gathered data, without relying on the priori knowledge on the sparsity of unknown sensing data. Our strategy minimizes the number of data ferries while guaranteeing the data quality by learning the statistical distribution of gathered data. Simulation results show that the proposed scheme improves the reconstruction quality compared to the existing ones.

In prediction-error expansion (PEE) based reversible data hiding, better exploiting image redundancy usually leads to a superior performance. However, the correlations among prediction-errors are not considered and utilized in current PEE based methods. Specifically, in PEE, the prediction-errors are modified individually in data embedding. In this paper, to better exploit these correlations, instead of utilizing prediction-errors individually, we propose to consider every two adjacent prediction-errors jointly to generate a sequence consisting of prediction-error pairs. Then, based on the sequence and the resulting 2D prediction-error histogram, a more efficient embedding strategy, namely, pairwise PEE, can be designed to achieve an improved performance. The superiority of our method is verified through extensive experiments.