Xiaoyi Zhou

Reversible data hiding in JPEG images is critical for secure multimedia applications, while, existing histogram modification schemes are confined to fixed 1D or 2D dimensions. These methods fail to adapt to the characteristics of cover images and the demands of embedded data, resulting in suboptimal trade-offs among embedding capacity, image quality, and file size. To address these issues, this study proposes a Multi-Dimensional Histogram Modification Framework that advances histograms from "fixed dimension" to "variable dimension" and extends them to high-dimensional structures. Our key contributions are as follows: first, we propose an adaptive dimension selection strategy that dynamically determines the optimal histogram dimension by evaluating the capacity and distortion for each cover image and embedding task; second, we develop a high-dimensional histogram construction method that combines AC coefficients with absolute values of 1 and 2 at the same frequency to enhance the utilization of coefficient correlation; third, we design an optimized mapping algorithm that searches for the optimal mapping matrix to minimize both distortion and file expansion. Experimental results show that the proposed method outperforms existing 1D and 2D histogram modification schemes. On average, it increases the embedding capacity by 5000 bits, improves the image quality by 0.2 dB, and reduces the file size by 4000 bits.

Diffusion models have garnered significant attention in deep generative modeling due to their exceptional ability to generate diverse and high-quality samples across various data modalities. However, their security vulnerabilities, particularly backdoor attacks, remain largely unexplored, resulting in unpredictable and potentially malicious image generation. This paper introduces SBADiffusion, an invisible backdoor attack method that integrates steganography and quantization techniques to embed undetectable triggers into diffusion models. Specifically, steganography generates triggers by embedding subtle noise patterns into images, enabling these triggers to carry secret information without being perceptible to the human eye. Quantization technique further enhances the stealth of the triggers by optimizing their embedding to minimize visible artifacts or irregularities. Through a meticulously designed framework, this method achieves a delicate balance between visual stealth and attack reliability. Experimental results demonstrate that even at a low poisoning rate of 10 %, the attack success rate exceeds 90 %, and the mean squared error (MSE) of the poisoned images is significantly lower than that of existing methods. These findings not only highlight the stealth and precision of the proposed method but also reveal its potential for malicious exploitation, underscoring the urgent need to develop robust defense mechanisms against such threats in diffusion models.

The double random phase encoding (DRPE) image encryption method has garnered significant attention in color image processing and optical encryption thanks to its R, G, and B parallel encryption. However, DRPE-based color image encryption faces two challenges. Firstly, it disregards the correlation of R, G, and B, compromising the encrypted image’s robustness. Secondly, DRPE schemes relying on Discrete Fourier Transform (DFT) and Discrete Fractional Fourier Transform (DFRFT) are vulnerable to linear attacks, such as Known Plaintext Attack (KPA) and Chosen Plaintext Attack (CPA). Quantum walk is a powerful tool for modern cryptography, offering robust resistance to classical and quantum attacks. Therefore, this study presents an optical color image encryption algorithm that combines two-dimensional quantum walking (TDQW) with 24-bit plane permutation, dubbed OCT. This approach employs pseudo-random numbers generated by TDQW for phase modulation in DRPE and scrambles the encrypted image’s real and imaginary parts using the generalized Arnold transform. The 24-bit plane permutation helps reduce the R, G, and B correlation, while the generalized Arnold transform bolsters DRPE’s resistance to linear attacks. By incorporating TDQW, the key space is significantly expanded. The experimental results validate the effectiveness and security of the proposed method.

The Double Random Phase Encoding (DRPE) image encryption method has garnered significant attention in color image processing and optical encryption, thanks to its parallel encryption of R, G, B. However, DRPE-based color image encryption faces two challenges. Firstly, it disregards the correlation of R, G, and B, compromising the encrypted image's robustness. Secondly, DRPE schemes relying on Discrete Fourier Transform (DFT) and Discrete Fractional Fourier Transform (DFRFT) are vulnerable to linear attacks, such as Known Plaintext Attack (KPA) and Chosen Plaintext Attack (CPA). Quantum walk stands out as a remarkable tool for designing modern cryptographic mechanisms, offering resistance to potential attacks from both classical and quantum computers.Therefore, this study presents an optical color image encryption algorithm that combines two-dimensional quantum walk with 24-bit plane permutation, dubbed OCT. This approach employs pseudo-random numbers generated by Two-Dimensional Quantum Walking (TDQW) for phase modulation in DRPE and scrambles the encrypted image's real and imaginary parts using the Generalized Arnold Transform. The 24-bit plane permutation helps reduce correlation of the R, G, B, while the Generalized Arnold Transform bolsters DRPE's resistance to linear attacks. By incorporating TDQW, the key space is significantly expanded. Experimental results validate the effectiveness and security of proposed method.