Jianzhong Li

An RGB-D image encryption scheme is presented by combining a newly constructed 4D-ICTM hyperchaotic system with a reformulated quaternion gyrator transform (QGT). The proposed 4D-ICTM dynamics arise from the nonlinear combination of the ICMIC and Chebyshev maps with the tanh function and modulo operation. Their dynamical and statistical properties are examined using Lyapunov exponents, bifurcation diagrams, histograms, autocorrelation analysis, and correlation-dimension computation. To handle the heterogeneous RGB-D data, a dedicated preprocessing procedure converts the RGB channels and 16-bit depth data into four 8-bit components, which are represented jointly in the quaternion domain. The reformulated QGT is evaluated in terms of numerical consistency and computational cost using numerical error, SSIM, and execution-time measurements. The resulting encryption approach is further assessed through statistical analysis, adjacent-pixel correlation, floating-frequency analysis, graphic autocorrelation, and decryption-error evaluation. The experiments show resistance to the tested attack scenarios together with competitive computational performance. An encryption throughput of 43.32 Mbps is obtained at 406 cycles/byte, indicating the potential of the proposed scheme for RGB-D image encryption applications.

The Segment Anything Model (SAM) demonstrated outstanding performance in 2D segmentation tasks, exhibiting robust generalization to natural images through its prompt-driven design. However, due to the lack of volumetric spatial information modeling and the domain gap between nature and medical images, its direct application to 3D medical image segmentation is suboptimal. Existing approaches to adapting SAM for 3D segmentation typically involve architectural adjustments by integrating additional components, thereby increasing trainable parameters and requiring higher GPU memory during fine-tuning. Moreover, retraining the prompt encoder may result in degraded spatial localization, especially when annotated data is scarce. To address these limitations, we propose ZAP-2.5DSAM, a parameter-efficient fine-tuning framework, which effectively extends the segmentation capacity of SAM to 3D medical images through a 2.5D decomposition scheme without introducing any additional adapter modules. Our method fine-tunes only 3.51M parameters from the original SAM, significantly reducing GPU memory requirements during training. Extensive experiments on multiple 3D tumor segmentation benchmarks demonstrate that ZAP-2.5DSAM achieves superior segmentation accuracy compared to conventional fine-tuning methods. Our code and models are available at: https://github.com/CaiGuoHS/ZAP-2.5DSAM.git.

In this paper, a watermark embedding scheme based on ghost image encryption and cellular automata transformation is proposed. In this scheme, the watermark forms speckle through different light intensities into a key, and the cellular automata transformation algorithm is embedded into the 3D image. Compared with the traditional watermarking encryption method, this scheme combines ghost imaging and the cellular automata transformation algorithm, which double guarantees and increases the confidentiality of the watermark. The binary computing ghost imaging discussed in this paper saves the storage space of password text and makes the transmission of password text more convenient and faster. Experiments on this method also verify that the watermark-embedded image has higher imperceptibility and higher robustness against attacks, and that the extracted watermark has good integrity.

A novel adaptive secure holographic image watermarking method in the sharp frequency localized contourlet transform (SFLCT) domain is presented. Based upon the sine logistic modulation map and the logistic map, we develop an encrypted binary computer-generated hologram technique to fabricate a hologram of a watermark first. Owing to the enormous key space of the encrypted hologram, the security of the image watermarking system is increased. Then the hologram watermark is embedded into the SFLCT coefficients with Schur decomposition. To obtain better imperceptibility and robustness, the entropy and the edge entropy are utilized to select the suitable watermark embedding positions adaptively. Compared with other watermarking schemes, the suggested method provides a better performance with respect to both imperceptibility and robustness. Experiments show that our watermarking scheme for images is not only is secure and invisible, but also has a stronger robustness against different kinds of attack.