At present, the development of deep forgery technology has brought new challenges to media content forensics, and the use of deep forgery identification methods to identify forged audio and video has become a significant focus of research and difficulty. Deep forgery technology and forensic technology play a mutual game and promote each other’s development. This paper proposes a spatiotemporal local feature abstraction (STLFA) framework for facial forgery identification to solve the media industry challenges of deep forgery technology. To adequately utilize local facial features, we combine facial key points, key point movement, and facial corner points to detect forgery content. This paper establishes a spatiotemporal relation, which realizes face forgery detection by identifying abnormalities of facial keypoints and corner points for interframe judgments. Meanwhile, we utilize RNNs to predict the sequences from facial key point movement abnormalities and corner points for interframe. Experimental results show that our method achieves better performance than some existing methods and good anticompression forgery face detection performance on FF++.

Media content forgery is widely spread over the Internet and has raised severe societal concerns. With the development of deep learning, new technologies such as generative adversarial networks (GANs) and media forgery technology have already been utilized for politicians and celebrity forgery, which has a terrible impact on society. Existing GAN-generated face detection approaches rely on detecting image artifacts and the generated traces. However, these methods are model-specific, and the performance is deteriorated when faced with more complicated methods. What’s more, it is challenging to identify forgery images with perturbations such as JPEG compression, gamma correction, and other disturbances. In this paper, we propose a global–local facial fusion network, namely GLFNet, to fully exploit the local physiological and global receptive features. Specifically, GLFNet consists of two branches, i.e., the local region detection branch and the global detection branch. The former branch detects the forged traces from the facial parts, such as the iris and pupils. The latter branch adopts a residual connection to distinguish real images from fake ones. GLFNet obtains forged traces through various ways by combining physiological characteristics with deep learning. The method is stable with physiological properties when learning the deep learning features. As a result, it is more robust than the single-class detection methods. Experimental results on two benchmarks have demonstrated superiority and generalization compared with other methods.

Nowadays, deepfake detection on subtle-expression manipulation, facial-detail modification, and smeared images has become a research hotspot. Existing deepfake-detection methods on the whole face are coarse-grained, where the details are missing due to the negligible manipulated size of the image. To address the problems, we propose to build a transformer model for a deepfake-detection method by organ, to obtain the deepfake features. We reduce the detection weight of defaced or unclear organs to prioritize the detection of clear and intact organs. Meanwhile, to simulate the real-world environment, we build a Facial Organ Forgery Detection Test Dataset (FOFDTD), which includes the images of mask face, sunglasses face, and undecorated face collected from the network. Experimental results on four benchmarks, i.e., FF++, DFD, DFDC-P, Celeb-DF, and for FOFDTD datasets, demonstrated the effectiveness of our proposed method.

Image manipulation methods, such as the copy-move, splicing, and removal methods, have become increasingly mature and changed the common perception of “seeing is believing.” The credibility of digital media has been seriously damaged with the development of image manipulation methods. Most image manipulation detection methods detect traces of tampering pixel by pixel. As a result, the detected manipulation areas are separated, which results in insufficient consideration of content manipulation at the object level. In this paper, the detection of image manipulation areas based on forgery object detection and pixel discrimination is proposed. Specifically, the pixel-level detection branch resamples features and uses an LSTM to detect manipulations, such as resampling, rotation, and cropping. The goal of the forgery object detection branch, which is based on Faster R-CNN, is to extract the regions of interest and analyze the regions with high contrast as well as the forgery objects of the image. Furthermore, the fused heatmaps of the two branches are integrated with the object detection results. The noise in the heatmaps is shielded based on the forgery object information of the region proposal network. Experimental results on multiple standard forgery datasets have demonstrated the superiority of our proposed method compared with the state-of-the-art methods.

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