Watermarking involves embedding a watermark in an image and later extracting it to prove the image’s copyright. In most cases, a complete image contains both smooth and textured regions. As a rule of thumb, the visual quality of an image with a watermark embedded in its textured regions is better than that of the same image with a watermark in smooth regions. This paper, by taking advantage of the fact, proposes a texture-aware local adaptive watermarking algorithm to maximize the watermark’s robustness while maintaining its imperceptibility. To identify textured regions in an image, we introduce the texture value, an efficient and proper metric of the richness of image texture. It combines the texture correlation of the AC coefficients, the luminance masking of the DC coefficient, and the distribution of image texture. A watermark is embedded adaptively into multiple non-overlapping textured regions of an image under the specified SSIM condition. Its adaptiveness comes from a novel texture-aware adaptive parameter model derived by multivariate regression analysis. Correct extraction of watermarks from multiple textured regions can be done by the cooperation of embedding and extraction strategies, with the assistance of RS-based watermark coding model. They allow for greater robustness, faster extraction, and adjustable watermark capacity. The simulation experiments on 100 images demonstrate that our proposed algorithm outperforms state-of-the-art algorithms with respect to imperceptibility, robustness, and adaptability.

With the development of digital technology, various fields generate and share a large amount of visual content. Image retrieval is a hot research direction in the field of computer vision. Efficient and accurate retrieval of query content from massive data is the ultimate form pursued by image retrieval technology. In recent years, the rise of deep learning technology has promoted the rapid development of the field of computer vision. Due to the powerful expressive ability of deep features on image content, image retrieval based on deep learning has become the most cutting-edge research direction in CBIR technology. This paper summarizes the relevant research on the classic deep learning image retrieval technology in recent years, first introduces the form of the CBIR problem, and then lists the classic datasets in this field. Afterwards, content-based deep image retrieval methods are reviewed from the perspectives of network models, deep feature extraction, and retrieval types. Finally, summarize the problems to be solved urgently in the current research, and look forward to the future research direction.

Video watermarking plays a vital role in protecting the video copyright. The quantization-based methods are widely used in the existing watermarking algorithms, owing to their low computational complexity and completely blind extraction. However, most of them work poorly in resisting scaling attacks, by which the quantization value may fall outside the original quantization interval. For addressing this issue, an adaptive quantization index modulation method is proposed. The property that is associated with the ratio of the DC coefficient before and after scaling the video resolution motivates us to select the DC coefficient as the quantization value and set the size of the quantization interval by the video resolution to maintain the synchronization between them before and after scaling. Moreover, a strategy taking advantage of the high decoding reliability of the QRCode is proposed to terminate the extraction in advance, and both the embedding and the extracting process are performed in the spatial domain, which all contribute to further enhance the execution efficiency. The experimental results show that our algorithm outperforms the state-of-the-art method in terms of imperceptibility, robustness, and computational cost.

Recently, deep learning has been gradually applied to digital watermarking, which avoids the trouble of hand-designing robust transforms in traditional algorithms. However, most of the existing deep watermarking algorithms use encoder–decoder architecture, which is redundant. This paper proposes a novel audio watermarking algorithm based on adversarial perturbation, AAW. It adds tiny, imperceptible perturbations to the host audio and extracts the watermark with a pre-trained decoder. Moreover, the AAW algorithm also uses an attack simulation layer and a whitening layer to improve performance. The AAW algorithm contains only a differentiable decoder, so it reduces the redundancy. The experimental results also demonstrate that the proposed algorithm is effective and performs better than existing audio watermarking algorithms.

With the power of deep learning, super‐resolution (SR) methods enjoy a dramatic boost in performance. However, they usually have a large model size and high computational complexity, which hinders the application in devices with limited memory and computing power. Some lightweight SR methods solve this issue by directly designing shallower architectures, but it will adversely affect the representation capability of convolutional neural networks. To address this issue, we propose the dual feature aggregation strategy for image SR. It enhances feature utilization via feature reuse, which largely improves the representation ability while only introducing marginal computational cost. Thus, a smaller model could achieve better cost‐effectiveness with the dual feature aggregation strategy. Specifically, it consists of Local Aggregation Module (LAM) and Global Aggregation Module (GAM). LAM and GAM work together to further fuse hierarchical features adaptively along the channel and spatial dimensions. In addition, we propose a compact basic building block to compress the model size and extract hierarchical features in a more efficient way. Extensive experiments suggest that the proposed network performs favorably against state‐of‐the‐art SR methods in terms of visual quality, memory footprint, and computational complexity.