In video encoding rate control, adaptive selection of the initial quantization parameter (QP) is a critical factor affecting both encoding quality and rate control precision. Due to the diversity of video content and the dynamic nature of network conditions, accurately and efficiently determining the initial QP remains a significant challenge. The optimal setting of the initial QP not only influences bitrate allocation strategies but also impacts the encoding efficiency and output quality of the encoder. To address this issue in the H.266/VVC standard, this paper proposes a novel hierarchical reinforcement learning-based method for adaptive initial QP selection. The proposed method introduces a hierarchical reinforcement learning framework that decomposes the initial QP selection task into high-level and low-level strategies, handling coarse-grained and fine-grained QP decisions, respectively. The high-level strategy quickly determines a rough QP range based on global video features and network conditions, while the low-level strategy refines the specific QP value within this range to enhance decision accuracy. This framework integrates spatiotemporal video complexity, network conditions, and rate control objectives to form an optimized model for adaptive initial QP selection. Experimental results demonstrate that the proposed method significantly improves encoding quality and rate control accuracy compared to traditional methods, confirming its effectiveness in handling complex video content and dynamic network environments.

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.

Reversible data hiding (RDH) in structured images remains challenging: most deep-learning methods target natural images, where distortions can be spread across the entire picture. When the carrier contains functionally critical regions—the finder and alignment patterns of a QR code, for instance—a uniform allocation of distortion directly hurts machine readability and, as a result, reversibility. This paper presents AR-WeNet, a structure-aware reversible data hiding framework for images with rigid functional structures. It brings together two components: (1) a Regulatory Information Encryption Module (RIEM) that turns a structured regulatory record into an encrypted binary pattern through UTF-8 encoding, QR error-correction coding, and Arnold scrambling; and (2) a DWT-domain invertible neural network backbone enhanced by Multi-Scale Wavelet Subband Attention (MSWA). A region-adaptive embedding (RAE) strategy, steered by a predefined structural weight map, concentrates embedding distortions in redundant data modules while leaving functional modules intact. On the self-constructed E-Voucher-500 benchmark, AR-WeNet reaches 63.29 dB PSNR, 0.9999 SSIM, a 0.000% pre-correction bit error rate, and 100% exact QR decoding success—an 8.00 dB improvement over the uniform-weighting WeNet baseline. Robustness tests under JPEG compression, Gaussian noise, and cropping confirm resilience at both the bit level and the decoding level. On DIV2K, the INN backbone achieves 55.27 dB, surpassing HiNet by 10.41 dB, showing that the design extends beyond structured images to natural-image hiding.

Adversarial attack methods have achieved satisfactory results in white-box attack scenarios, but their performance declines when transferred to other deep neural network (DNN) models. Currently, there are many methods to improve the transferability of adversarial samples, and enhancing transferability through input transformations is an effective approach. However, most existing input transformations are performed in the spatial domain, neglecting transformations in the frequency domain. Therefore, this paper proposes a novel input transformation-based attack: the frequency domain enhancement (FDE) method, which performs input transformations in the frequency domain to increase input diversity. Specifically, this method processes input images in the frequency domain, suppresses high-frequency information in the input images, and then randomly amplifies certain frequency domain information, generating adversarial samples with stronger transferability. Experimental results show that adversarial samples generated through FDE demonstrate significant improvement in transferability on both undefended and defended models on the ImageNet dataset. Notably, this method can be combined with many existing techniques to further enhance the transferability of adversarial samples.