The research on forgery detection and localization is significant in digital forensics and has attracted increasing attention recently. Traditional methods mostly use handcrafted or shallow-learning based features, but they have limited description ability and heavy computational costs. Recently, deep neural networks have shown to be capable of extracting complex statistical features from high-dimensional inputs and efficiently learning their hierarchical representations. In order to capture more discriminative features between tampered and non-tampered regions, we propose an improved mask regional convolutional neural network (Mask R-CNN) which attach a Sobel filter to the mask branch of Mask R-CNN in this paper. The Sobel filter acts as an auxiliary task to encourage predicted masks to have similar image gradients to the groundtruth mask. The overall network is capable of detecting two different types of image manipulations, including copy-move and splicing. The experimental results on two standard datasets show that the proposed model outperforms some state-of-the-art methods.

Image inpainting can effectively repair damaged areas, but it can also be a way of image tampering when it is used to remove meaningful content from an image. Therefore, this paper focuses on the research of inpainting forensics, and proposes a multi-task deep learning method. In order to enhance the learning of texture features, the corresponding local binary pattern channels are added to the input of the network. Furthermore, considering that the multi-task object detection network Mask R-CNN cannot fully utilize the features of all scale feature information during the FPN feature extraction phase, the network in this paper combines Feature Pyramid Networks and back connections to extract more features. This network model can detect not only the images tampered by traditional inpainting methods, but also the images inpainted by deep learning methods. Experimental results on two large public data sets demonstrate the superior performance of the proposed method.

Image inpainting algorithms have a wide range of applications, which can be used for object removal in digital images. With the development of semantic level image inpainting technology, this brings great challenges to blind image forensics. In this case, many conventional methods have been proposed which have disadvantages such as high time complexity and low robustness to postprocessing operations. Therefore, this paper proposes a mask regional convolutional neural network (Mask R-CNN) approach for patch-based inpainting detection. According to the current research, many deep learning methods have shown the capacity for segmentation tasks when labeled datasets are available, so we apply a deep neural network to the domain of inpainting forensics. This deep learning model can distinguish and obtain different features between the inpainted and noninpainted regions. To reduce the missed detection areas and improve detection accuracy, we also adjust the sizes of the anchor scales due to the inpainting images and replace the original nonmaximum suppression single threshold with an improved nonmaximum suppression (NMS). The experimental results demonstrate this intelligent method has better detection performance over recent approaches of image inpainting forensics.

As Deepfake technology continues to evolve, the distinction between real and fake content becomes increasingly blurred. Most existing Deepfake video detection methods rely on single-frame facial image features, which limits their ability to capture temporal differences between frames. Current methods also exhibit limited generalization capabilities, struggling to detect content generated by unknown forgery algorithms. Moreover, the diversity and complexity of forgery techniques introduced by Artificial Intelligence Generated Content (AIGC) present significant challenges for traditional detection frameworks, which must balance high detection accuracy with robust performance. To address these challenges, we propose a novel Deepfake detection framework that combines a two-stream convolutional network with a Vision Transformer (ViT) module to enhance spatio-temporal feature representation. The ViT model extracts spatial features from the forged video, while the 3D convolutional network captures temporal features. The 3D convolution enables cross-frame feature extraction, allowing the model to detect subtle facial changes between frames. The confidence scores from both the ViT and 3D convolution submodels are fused at the decision layer, enabling the model to effectively handle unknown forgery techniques. Focusing on Deepfake videos and GAN-generated images, the proposed approach is evaluated on two widely used public face forgery datasets. Compared to existing state-of-the-art methods, it achieves higher detection accuracy and better generalization performance, offering a robust solution for deepfake detection in real-world scenarios.

In this study, the authors introduce a novel deep generative adversarial networks (GANs) model for global mosaic removal. The methods used in the proposed study consist of GANs model and a novel algorithm for maintaining and repairing (MR) images. The conventional mosaic removal algorithms all employ the correlation between the inserted pixel and its neighbouring pixels, which have a limited effect on the local mosaic removal but do not work well for the global mosaic removal. To respond to this difficulty, the authors introduce an MRGAN model with two novel parsing networks. Unlike previous GANs, the MR algorithm is used to calculate the pixel loss and content loss. The experimental comparison results show that the proposed MRGAN model has achieved leading results for the global mosaic removal task.