Under the framework of computational integral imaging, a multi-image encryption scheme based on the DNA-chaos algorithm is proposed. In this scheme, multiple images are merged to one image by a computational integral imaging algorithm, which significantly improves the efficiency of image encryption. Meanwhile, the computational integral imaging algorithm can merge images at different depth distances, thereby the different depth distances of multiple images can also be used as keys to increase the security of the encryption method. In addition, the high randomness of the chaos algorithm is combined to address the outline effect caused by the DNA encryption algorithm. We have experimentally verified the proposed multi-image encryption scheme. The entropy value of the encrypted image is $7.6227$, whereas the entropy value of the merge image with two input images is $3.2886$, which greatly reduces the relevance of the image. The simulation results also confirm that the proposed encryption scheme has high key security and can protect against various attacks.

Light Field (LF) images provide rich visual representations of 3D scenes by capturing both spatial and angular information of light rays. However, their high dimensions present substantial challenges for conventional 2D image watermarking techniques in effectively ensuring copyright protection. In this work, we propose a deep learning-based Spatial-Angular Consistency waterMarking (SACMark) network, designed to address the unique challenges of watermark embedding and extraction in LF images. SACMark employs a spatial-angular feature extraction module to capture the multidimensional information of LF images and introduces consistency matching and fusion strategies to enhance feature utilization. The network adopts an encoder-noise-decoder architecture, optimized through adversarial training to improve the imperceptibility and robustness of the watermark. Experimental results demonstrate that SACMark maintains high visual quality across various embedding capacities and has minimal impact on depth estimation. Compared to traditional LF watermarking approaches and existing deep learning-based methods for 2D images, SACMark demonstrates improved resilience to noise while preserving essential LF characteristics. These findings suggest that SACMark holds promise for practical applications and may contribute to future developments in secure and adaptive LF image protection.

In recent years, multi-modal fake news detection methods have been widely used and have shown good detection performance in identifying common fake news scenarios, i.e., different tweets are accompanied by different images. However, many fake news stories involve multiple tweets using the same image. Existing detection methods do not fully consider this feature, resulting in unsatisfactory performance in this case. To address this omission problem, we proposed a Multi-modal Iterative Guided Framework (MIGF), where the entire detection process is completed in the latent space. Specifically, first, we construct a latent space at the feature level to alleviate the problem of inconsistent multi-modal feature space and distribution. Then, at the multi-feature interaction level, a multi-modal cross-guided attention mechanism is designed for the interaction of text and visual features. Finally, in terms of multi-view feature extraction and learning, an iterative guided mixture-of-expert system is used to guide the extraction of multi-modal features. Comparative experiments show that the comprehensive performance of MIGF surpasses the baseline models, verifying its effectiveness and superiority. Ablation experiments further quantify the contribution of each module, proving that the overall architecture is indispensable.