In recent years, reversible data hiding technology has been widely used in JPEG images for special purposes such as file management and image authentication. Histogram shifting is one of the most popular techniques for achieving reversible data hiding technology. However, invalid shifting in histogram shifting limits the performance of existing reversible data hiding schemes. Therefore, we propose a two-dimensional histogram shifting-based reversible data hiding scheme in this article to improve the performance of marked JPEG images in terms of visual quality and file size. In the proposed histogram shifting method, only the coefficient pairs containing two non-zero quantized discrete cosine transform coefficients are changed for embedding data. Specifically, the coefficient pairs with at least one quantized discrete cosine transform coefficient valued −1 or +1 are shifted and the rests leave room for embedding data. With our proposed reversible data hiding scheme, the number of invalid shifting pixels is reduced so that it improves the performance of marked JPEG images. The experimental results show that the proposed method achieves a higher peak signal-to-noise ratio and has a lower increase in file size than state-of-art methods.

Infrared and visible image fusion aims to generate synthetic images including salient targets and abundant texture details. However, traditional techniques and recent deep learning-based approaches have faced challenges in preserving prominent structures and fine-grained features. In this study, we propose a lightweight infrared and visible image fusion network utilizing multi-scale attention modules and hybrid dilated convolutional blocks to preserve significant structural features and fine-grained textural details. First, we design a hybrid dilated convolutional block with different dilation rates that enable the extraction of prominent structure features by enlarging the receptive field in the fusion network. Compared with other deep learning methods, our method can obtain more high-level semantic information without piling up a large number of convolutional blocks, effectively improving the ability of feature representation. Second, distinct attention modules are designed to integrate into different layers of the network to fully exploit contextual information of the source images, and we leverage the total loss to guide the fusion process to focus on vital regions and compensate for missing information. Extensive qualitative and quantitative experiments demonstrate the superiority of our proposed method over state-of-the-art methods in both visual effects and evaluation metrics. The experimental results on public datasets show that our method can improve the entropy (EN) by 4.80%, standard deviation (SD) by 3.97%, correlation coefficient (CC) by 1.86%, correlations of differences (SCD) by 9.98%, and multi-scale structural similarity (MS_SSIM) by 5.64%, respectively. In addition, experiments with the VIFB dataset further indicate that our approach outperforms other comparable models.

With the rapid development of multimedia editing technology and DeepFake technology, image integrity and authenticity meet more challenges. Most existing methods only focus on improving the accuracy of tamper detection and localization, but ignore the potential tampering risk, which is related to the saliency. There are uneven potential tamper threats to any graphic images, and it is interesting to exploit saliency to adaptively assign embedding cost. We propose an active forensics scheme for tamper localization by adaptively adjusting cost assignment. The experimental results demonstrate a significant improvement in transparency, localization accuracy, and robustness against unintentional attacks.