With the rapid development of artificial intelligence technology, a variety of GAN generated deepfake face images/videos have emerged endlessly. The abuse of deepfake has brought serious negative effects to many industries. Therefore, there is an urgent need to develop advanced methods to combat the abuse of deepfake. As far as we know, there are almost no techniques that can distinguish multiple types of homologous deepfake face images. In this study, we propose a method based on the multi-classification task to address this issue. The proposed method relies on a novel network framework named FCD-Net that consists of the facial synaptic saliency module (FSS), the contour detail feature extraction module (CDFE), and the distinguishing feature fusion module (DFF). Utilizing this method, the imperceptible features introduced by deepfake can be exposed, and the differences caused by different types of deepfake can be distinguished, even if deepfake images are homologous. To test the proposed method and compare it with other SOTA methods, we establish a new homologous dataset named HDFD that contains real face images, entire face synthesis images, face swap images, and facial attribute manipulation images. Among them, the three types of deepfake images are all generated from the same real face images through different deepfake techniques. Abundant experiment results demonstrate that the proposed method has a high-level detection accuracy and relatively strong robustness against content-preserving manipulations. Moreover, the generalization of our method is superior to other SOTA methods.

Modern image editing software enables anyone to alter the content of an image to deceive the public, which can pose a security hazard to personal privacy and public safety. The detection and localization of image tampering is becoming an urgent issue to be addressed. We have revealed that the tampered region exhibits homogenous differences (the changes in metadata organization form and organization structure of the image) from the real region after manipulations such as splicing, copy-move, and removal. Therefore, we propose a novel end-to-end network named HDF-Net to extract these homogeny difference features for precise localization of tampering artifacts. The HDF-Net is composed of RGB and SRM dual-stream networks, including three complementary modules, namely the suspicious tampering-artifact prominent (STP) module, the fine tampering-artifact salient (FTS) module, and the tampering-artifact edge refined (TER) module. We utilize the fully attentional block (FLA) to enhance the characterization ability of homogeny difference features extracted by each module and preserve the specifics of tampering artifacts. These modules are gradually merged according to the strategy of “coarse-fine-finer”, which significantly improves the localization accuracy and edge refinement. Extensive experiments demonstrate that HDF-Net performs better than state-of-the-art tampering localization models on five benchmarks, achieving satisfactory generalization and robustness.

The object of image super-resolution reconstruction is to overcome the limitations imposed by hardware imaging conditions and patterns, aiming to restore high-frequency details in images through signal processing techniques. Recently, deep learning-based single-image super-resolution reconstruction (SISR) has achieved remarkable performance. However, the current methods exhibit inadequate performance in the reconstruction of texture details, thereby posing a challenge for further enhancing the accuracy of super-resolution reconstruction. In this study, we propose a novel contextual texture enhancement network (CTE-Net) aimed at improving the level of texture details in image super-resolution. The CTE-Net comprises of two crucial components: the multi-level feature aggregation module (MFAM) and the contextual information enhancement module (CIEM). The MFAM integrates global and local low-resolution (LR) features from both the pixel space and channel dimensions, thereby enhancing the feature representation capability of the network. The CIEM is deployed to enhance the network's learning capacity by integrating a meticulously designed context-attention mechanism, which effectively explores the adjacent contextual information of images and thereby amplifies the expressive capability of the generated features. Moreover, we utilize local binary patterns (LBP) to guide the feature selection strategies for MFAM and CIEM, thereby prioritizing the network's decision logic towards the recovery of texture details. The extensive experiments demonstrate that our method yields satisfactory results. In comparison to the state-of-the-art approaches, our method exhibits superior performance on the benchmark datasets.

Ensuring the privacy and secrecy of digital medical images has become a pressing issue as a result of the quick development of smart medical technology and the exponential growth in the quantity of medical images transmitted and stored in networks. The lightweight multiple-image encryption approach for medical images that is suggested in this research can encrypt/decrypt any number of medical photos of varied sizes with just one encryption operation and has a computational cost that is similar to encrypting a single image. The plaintext images with different sizes are filled at the right and bottom of the image to ensure that the size of all plaintext images is uniform; then, all the filled images are stacked to obtain a superimposed image. The initial key, which is generated using the SHA-256 technique, is then used as the starting value of the linear congruence algorithm to create the encryption key sequence. The cipher picture is then created by encrypting the superimposed image with the encryption key and DNA encoding. The algorithm can be made even more secure by implementing a decryption mechanism that decrypts the image independently in order to reduce the possibility of information leaking during the decryption process. The outcomes of the simulation experiment demonstrate the algorithm’s strong security and resistance to interference such as noise pollution and lost image content.