Fooling deep neural networks (DNNs) with black-box optimization has become a popular adversarial attack fashion, as the prior structural knowledge of DNNs is always unknown. Nevertheless, recent black-box adversarial attacks may struggle to balance their attack ability and visual quality of the generated adversarial examples (AEs) in tackling high-resolution images. In this paper, we propose an attention-guided black-box adversarial attack based on the large-scale multiobjective evolutionary optimization, termed LMOA. By considering the spatial semantic information of images, we first take advantage of the attention map to determine the perturbed pixels. Instead of attacking the entire image, reducing the perturbed pixels with the attention mechanism can help to avoid the notorious curse of dimensionality and thereby improve the performance of attacking. Second, a large-scale multiobjective evolutionary algorithm traverse the reduced pixels in the salient region. Benefiting from its characteristics, the generated AEs can fool target DNNs while being invisible by human vision. Extensive experimental results have verified the effectiveness of the proposed LMOA on the ImageNet data set. More importantly, it is more competitive to generate high-resolution AEs with the better visual quality than the existing black-box adversarial attacks.

As a branch of reversible data hiding (RDH), lossless data hiding (LDH) technique is important especially. Because LDH can not only reconstruct the cover image losslessly but also keep the visual quality of the marked image no degradation. This article proposes a new LDH scheme for JPEG images by general variable length code (VLC) mapping. In this scheme, the run size values are first reordered. Then a feasible solution space is generated. A simulated embedding model is established to find the optimal solution from the feasible solution space. The optimal mapping relationship is constructed according to the optimal solution. According to the optimal GVM relationship, the Huffman table in the file header is modified and then the additional data can be embedded by replacing the used VLC with the VLCs in the same mapping set. Experimental results demonstrate that most of the JPEG images using the proposed scheme cause less file size increments than previous RDH schemes while keeping the marked JPEG image with no distortion. Furthermore, the proposed scheme can obtain high embedding capacity.