Kaimeng Chen

Coding scheme is the fundamental part of steganography to embed message in cover data arousing as less distortion as possible. In some recent developed robust steganographic methods, it also undertakes error-correction on message extracted from stego data transmitted in lossy communication channel. This paper proposes Non-Binary Polar Codes (NBPCs) based stego codes which could directly implement multi-layered steganographic embedding without multi-layered decomposition. Moreover, we extend proposed scheme for robust steganography, which has the scalable ability of error-correcting and avoids the problem of allocating embedded redundant payload in multiple layers for error-correcting. By analyzing steganographic embedding and error-correcting for robust steganography, we find that both tasks are adequate to be solved in identical form of Maximum A Posteriori decoding (MAP decoding). Consequently, we develop successive cancellation list MAP decoding for NBPCs built on Galois Field. Besides, we propose a quick estimation method to directly estimate joint probabilities of modification on multi-layer bits to accelerate proposed coding scheme. Compared with existing coding schemes for steganography and robust steganography, such as double-layered STCs, SPCs and doublelayered dual-STCs, the experimental results demonstrate that our method achieves lower distortion, higher time efficiency, and better error-correcting performance.

This paper proposes a novel method of vacatingroom-after-encryption reversible data hiding in encrypted image (VRAE RDHEI), which uses the ideas of channel modeling and channel coding to achieve the enhancement of capacity. The framework of the proposed method maps the processes of data embedding and image recovery to a virtual noisy channel for transmitting special flag bits of image content, and then it uses the systematic polar code to ensure error-free transmission for reversible data hiding. On the data hider side, to reversibly vacate room for secret data, the selected bits of the encrypted image are transformed to flag bits and then encoded to fewer parity bits by systematic polar code. On the receiver side, the secret data can be extracted without error and separate from image recovery. To recover the image, the receiver uses pixel prediction to obtain the noisy flag bits and decodes them to the original flag bits by a special channel knowledge-based decoding algorithm with the parity bits. Then, the original image can be recovered by the flag bits. The experimental results prove that the proposed method outperforms the state-of-the-art VRAE methods.

As cloud services rapidly evolve and the demand for privacy protection grows, reversible data hiding in encrypted images (RDHEI) has gained significant attention. To enhance the data embedding capacity of RDHEI, this paper proposes an adaptive prediction-error entropy encoding framework that dynamically allocates edge-directed prediction (EDP) errors to either separate or grouped entropy coding modes, thereby optimizing net payload capacity. The image owner first predicts the pixel values of the cover image using EDP algorithm and calculates the prediction errors. After computing the prediction errors, the optimal thresholds are adaptively determined by minimizing the expected codeword length through entropy coding theory. Using these optimized thresholds, the prediction errors are classified into separate or grouped encoding categories, and losslessly compressed via arithmetic encoding. Through the processes of image encryption and self-embedding, an encrypted image with embedding room is generated and subsequently uploaded to the cloud server. The data hider can easily locate the data embedding room in the encrypted domain of the image and embed encrypted additional data to obtain the marked encrypted image. The authorized recipient can correctly extract the embedded data, restore the original plaintext image without any loss, or do both. The experimental results demonstrate the effectiveness of the proposed approach, surpassing many state-of-the-art RDHEI techniques.

The data embedding of vacating room after encryption reversible data hiding in encrypted images (VRAE RDHEI) is performed on an encrypted image without redundancy and spatial correlation. Data extraction and image recovery rely on a range of unique mechanisms that utilize spatial correlation in the decrypted domain. Of these mechanisms, pixel prediction is among the most frequently used, directly affecting the capacity and fidelity. In this paper, we propose a novel method that uses a two-round interpolation mechanism to enhance pixel prediction precision while preserving a large number of carrier pixels. In the proposed method, the content owner uses a stream cipher to encrypt the image as a carrier. The data hider flips specific LSBs of the encrypted image for data embedding. On the receiver side, the process of data extraction and image recovery is divided into two stages. In each stage, based on the varying distributions of the original or recovered pixels with the carrier pixels, the corresponding pixel interpolation schemes are used to accurately predict the pixels for data extraction and image recovery. The results demonstrate that the proposed method can efficiently improve the capacity and fidelity with full reversibility compared to existing VRAE RDHEI methods.

Small target detection and tracking has long been a difficult and research hotspot in computer vision. To solve the problem of low detection probability and high false alarm rate of traditional DP-TBD algorithm with high background complexity and low signal-to-noise ratio (SNR), a small video target tracking algorithm based on subspace model is proposed. The algorithm firstly adopts the detection model based on PCA-SIFT to preprocess the frame image, thereby obtaining a new measurement model. Then, according to the motion characteristics of the target and noise, as well as the correlation between the value functions of adjacent frames, a new value function is constructed, and multi-frame accumulation of the new value function is carried out using the DP-TBD algorithm. Thus, tracking before detection of dim and small targets can be realized. The simulation results show that the algorithm can track dim and small targets effectively under the condition of constant false alarm rate, and the detection probability is also improved.

For vacating-room-after-encryption reversible data hiding in encrypted images (VRAE RDHEI), an essential problem is how to address potential errors in data extraction and image recovery. This problem significantly limits the capacities of the existing VRAE RDHEI methods. To solve the problem while losing as little capacity as possible, in this paper, a novel method is proposed that uses the ideas of noisy channel model and channel code. By designing the data hiding mechanism appropriately, the embedding and extraction of data in the proposed method can be equivalent to the input and output of a virtual binary symmetric channel (BSC) model, so that the errors in data extraction are equivalent to the bit transitions in BSC. Based on the virtual BSC model, polar code is used to encode the secret data in the data hider's side. With the help of polar code, the receiver can decode the extracted bits containing errors to obtain correct secret data, then recover the error-free original image based on the corrected secret data. The experimental results proved that, compared with the existing VRAE methods, the proposed method can significantly improve the capacity and the quality of the decrypted images under the premise of complete reversibility.

JPEG steganalysis aims to detect stego JPEG images. For some robust steganography methods, in order to enhance stego images’ robustness of resisting JPEG recompression from lossy channel such SNS or photo sharing websites, steganographer may intentionally recompress cover image several times with quantization matrix of targeted channel, which thereby make it possible to transmit stego data in such channel for better disguise. In addition, there are huge number of cover JPEG images may be recompressed for various reasons, such as processing by some tools. Thus a better steganalysis method for such images is needed. In this paper, we investigate the steganalysis method for images recompressed with the same quantization matrix, namely, discriminate recompressed JPEG cover images and its stego images. We present some observed phenomenon on recompressed JPEG images, and design methods to enhance the sensitivity of feature based and deep model based steganalysis methods for this task. To verify their effectiveness with different acquisition of recompression prior-knowledge, we conduct experiments in various settings including conventional setting and mixing samples of different recompressing times in training. Their results demonstrate that the proposed method can notably improve detection accuracy on recompressed JPEG images.

Reversible data hiding in encrypted images (RDHEI) is a technology which embeds secret data into encrypted images in a reversible way. In this paper, we proposed a novel high-capacity RDHEI method which is based on the compression of prediction errors. Before image encryption, an adaptive linear regression predictor is trained from the original image. Then, the predictor is used to obtain the prediction errors of the pixels in the original image, and the prediction errors are compressed by Huffman coding. The compressed prediction errors are used to vacate additional room with no loss. After image encryption, the vacated room is reserved for data embedding. The receiver can extract the secret data and recover the image with no errors. Compared with existing approaches, the proposed method efficiently improves the embedding capacity.