In recent years, related research has focused on how to safely transfer and protect the privacy of images in social network services while providing easy access by authorized users. To safeguard privacy, we suggest an image encryption scheme that combines data hiding and image encryption. The proposed scheme successfully decrypts images after JPEG compression attacks and preserves the privacy of secret regions through the use of block scrambling encryption based on region selection. Simultaneously, the scheme can handle nonuniform secret regions and obtain more sensitive secret keys because of the incorporation of a chaotic system. The enhanced deep learning-based data-hiding technology reduces algorithm complexity by enabling the encryption position to be determined in the decryption phase without the need for any information or equipment. However, this approach also increases algorithm security, because only when the right secret data are extracted can they be decrypted successfully. According to the experimental findings, the proposed scheme can correctly decrypt images via JPEG compression while maintaining visually acceptable quality. The proposed scheme can achieve greater robustness against image processing algorithms and a wider secret key space than traditional schemes.

In recent years, advancements in deep Convolutional Neural Networks (CNNs) have brought about a paradigm shift in the realm of image super-resolution (SR). While augmenting the depth and breadth of CNNs can indeed enhance network performance, it often comes at the expense of heightened computational demands and greater memory usage, which can restrict practical deployment. To mitigate this challenge, we have incorporated a technique called factorized convolution and introduced the efficient Cross-Scale Interaction Block (CSIB). CSIB employs a dual-branch structure, with one branch extracting local features and the other capturing global features. Interaction operations take place in the middle of this dual-branch structure, facilitating the integration of cross-scale contextual information. To further refine the aggregated contextual information, we designed an Efficient Large Kernel Attention (ELKA) using large convolutional kernels and a gating mechanism. By stacking CSIBs, we have created a lightweight cross-scale interaction network for image super-resolution named “CSINet”. This innovative approach significantly reduces computational costs while maintaining performance, providing an efficient solution for practical applications. The experimental results convincingly demonstrate that our CSINet surpasses the majority of the state-of-the-art lightweight super-resolution techniques used on widely recognized benchmark datasets. Moreover, our smaller model, CSINet-S, shows an excellent performance record on lightweight super-resolution benchmarks with extremely low parameters and Multi-Adds (e.g., 33.82 dB@Set14 × 2 with only 248 K parameters).

Industrial image anomaly detection (IAD) is crucial for quality control, but its performance often degrades when training data contain label noise. To circumvent the reliance on potentially flawed labels, unsupervised methods that learn from the data distribution itself have become a mainstream approach. Among various unsupervised techniques, student–teacher frameworks have emerged as a highly effective paradigm. Student–Teacher Feature Pyramid Matching (STPM) is a powerful method within this paradigm, yet it is susceptible to such noise. Inspired by STPM and aiming to solve this issue, this paper introduces Meta-Learning Guided STPM (MLG-STPM), a novel framework that enhances STPM’s robustness by incorporating a guidance mechanism inspired by meta-learning. This guidance is achieved through an Evolving Meta-Set (EMS), which dynamically maintains a small high-confidence subset of training samples identified by their low disagreement between student and teacher networks. By training the student network on a combination of the current batch and the EMS, MLG-STPM effectively mitigates the impact of noisy labels without requiring an external clean dataset or complex re-weighting schemes. Comprehensive experiments on the MVTec AD and VisA benchmark datasets with synthetic label noise (0% to 20%) demonstrate that MLG-STPM significantly improves anomaly detection and localization performance compared to the original STPM, especially under higher noise conditions, and achieves competitive results against other relevant approaches.

Image steganography is often employed in information security and confidential communications, yet it typically faces challenges of imperceptibility and robustness during transmission. Meanwhile, insufficient attention has been paid to preserving the quality of the secret image after JPEG compression at the receiver, which limits the effectiveness of steganography. In this study, we propose an anti-compression attention-based diffusion pattern steganography model using GAN (ADPGAN). ADPGAN leverages dense connectivity to fuse shallow and deep image features with secret data, achieving high robustness against JPEG compression. Meanwhile, an enhanced attention module and a discriminator are employed to minimize image distortion caused by data embedding, thereby significantly improving the imperceptibility of the host image. Based on ADPGAN, we propose a novel JPEG-compression-resistant image framework that improves the quality of the recovered image by ensuring that the degradation of the reconstructed image primarily stems from sampling rather than JPEG compression. Unlike direct embedding of full-size secret images, we downsample the secret image into a secret data stream and embed it into the cover image via ADPGAN, demonstrating high distortion resistance and high-fidelity recovery of the secret image. Ablation studies validate the effectiveness of ADPGAN, achieving a 0-bit error rate (BER) under JPEG compression at a quality factor of 20, yielding an average Peak Signal-to-Noise Ratio (PSNR) of 39.70 dB for the recovered images.