The transferability of adversarial examples has been proven to be a potent tool for successful attacks on target models, even in challenging black-box environments. However, the majority of current research focuses on non-targeted attacks, making it arduous to enhance the transferability of targeted attacks using traditional methods. This paper identifies a crucial issue in existing gradient iteration algorithms that generate adversarial perturbations in a fixed manner. These perturbations have a detrimental impact on subsequent gradient computations, resulting in instability of the update direction after momentum accumulation. Consequently, the transferability of adversarial examples is negatively affected. To overcome this issue, we propose an approach called Adversarial Perturbation Transform (APT) that introduces a transformation to the perturbations at each iteration. APT randomly samples clean patches from the original image and replaces the corresponding patches in the iterative output image. This transformed image is then used to compute the next momentum. In addition, APT could seamlessly integrate with other iterative gradient-based algorithms, incurring minimal additional computational overhead. Experimental results demonstrate that APT significantly enhances the transferability of targeted attacks when combined with traditional methods. Our approach achieves this improvement while maintaining computational efficiency.

With the development of cloud computing, high-capacity reversible data hiding in an encrypted image (RDHEI) has attracted increasing attention. The main idea of RDHEI is that an image owner encrypts a cover image, and then a data hider embeds secret information in the encrypted image. With the information hiding key, a receiver can extract the embedded data from the hidden image; with the encryption key, the receiver reconstructs the original image. In this paper, we can embed data in the form of random bits or scanned documents. The proposed method takes full advantage of the spatial correlation in the original images to vacate the room for embedding information before image encryption. By jointly using Sudoku and Arnold chaos encryption, the encrypted images retain the vacated room. Before the data hiding phase, the secret information is preprocessed by a halftone, quadtree, and S-BOX transformation. The experimental results prove that the proposed method not only realizes high-capacity reversible data hiding in encrypted images but also reconstructs the original image completely.

Facial Expression Recognition (FER) is a research topic of great practical significance. However, existing FER methods still face numerous challenges, particularly in the interaction between spatial and global information, the distinction of subtle expression features, and the attention to key facial regions. This paper proposes a lightweight Global-Aware Spatial (GAS) Attention module, designed to improve the accuracy and robustness of FER. This module extracts global semantic information from the image via global average pooling and fuses it with local spatial features extracted by convolution, guiding the model to focus on regions highly relevant to facial expressions (such as the mouth and eyes). This effectively suppresses background noise and enhances the model’s ability to perceive subtle expression variations. In addition, we further introduce a Squeeze-and-Excitation (SE) Attention module into the dual-branch architecture to adaptively adjust the channel-wise weights of features, emphasizing critical region information and enhancing the model’s discriminative capacity. Based on these improvements, we develop the Ada-DF++ network model. Experimental results show that the improved model achieves test accuracies of 89.21%, 66.14%, and 63.75% on the RAF-DB, AffectNet (7cls), and AffectNet (8cls) datasets, respectively, outperforming current state-of-the-art methods across multiple benchmarks and demonstrating the effectiveness of the proposed approach for FER tasks.

Loop selection for multilevel nested loops is a very difficult problem, for which solutions through the underlying hardware-based loop selection techniques and the traditional software-based static compilation techniques are ineffective. A genetic algorithm- (GA-) based method is proposed in this study to solve this problem. First, the formal specification and mathematical model of the loop selection problem are presented; then, the overall framework for the GA to solve the problem is designed based on the mathematical model; finally, we provide the chromosome representation method and fitness function calculation method, the initial population generation algorithm and chromosome improvement methods, the specific implementation methods of genetic operators (crossover, mutation, and selection), the offspring population generation method, and the GA stopping criterion during the GA operation process. Experimental tests with the SPEC2006 and NPB3.3.1 standard test sets were performed on the Sunway TaihuLight supercomputer. The test results indicated that the proposed method can achieve a speedup improvement that is superior to that by the current mainstream methods, which confirm the effectiveness of the proposed method. Solving the loop selection problem of multilevel nested loops is of great practical significance for exploiting the parallelism of general scientific computing programs and for giving full play to the performance of multicore processors.

This paper analyzes random bits and scanned documents, two forms of secret data. The secret data were pre-processed by halftone, quadtree, and S-Box transformations, and the size of the scanned document was reduced by 8.11 times. A novel LSB matching algorithm with low distortion was proposed for the embedding step. The golden ratio was firstly applied to find the optimal embedding position and was used to design the matching function. Both theory and experiment have demonstrated that our study presented a good trade-off between high capacity and low distortion and is superior to other related schemes.

With the development of internet, digital media can be manipulated, reproduced, and distributed conveniently over networks. However, illegal copy, transmission and distribution of digital media become an important security issue. In this paper, we propose a high-capacity and robustness method based on discrete wavelet transforms(DWT) and optimal discrete cosine transforms(DCT). We present two approaches, they are: DWT-ODCT(high-capacity and robust image watermarking algorithm based on DWT and optimal DCT), and(P-DWT-ODCT) high-capacity and robust image watermarking algorithm based on DWT and optimal DCT by watermark preprocessing. The watermark image is preprocessed by halftone and quad tree techniques, and the position information about the content is extracted as the actual embedded value. The cover image is transformed by DWT and optimal DCT, which provides high imperceptibility and the least image distortion. Watermark bits are not directly inserted into the frequency coefficient, but embedded by modifying the coefficient according to some rules. With this method, regardless of the approach used, our study are higher capacity and robustness than the existing schemes. The watermark extraction produces high image quality after a variety of attacks.