In this work, we describe a new deep learning based method that can effectively distinguish AI-generated fake videos (referred to as DeepFake videos hereafter) from real videos. Our method is based on the observations that current DeepFake algorithm can only generate images of limited resolutions, which need to be further warped to match the original faces in the source video. Such transforms leave distinctive artifacts in the resulting DeepFake videos, and we show that they can be effectively captured by convolutional neural networks (CNNs). Compared to previous methods which use a large amount of real and DeepFake generated images to train CNN classifier, our method does not need DeepFake generated images as negative training examples since we target the artifacts in affine face warping as the distinctive feature to distinguish real and fake images. The advantages of our method are two-fold: (1) Such artifacts can be simulated directly using simple image processing operations on a image to make it as negative example. Since training a DeepFake model to generate negative examples is time-consuming and resource-demanding, our method saves a plenty of time and resources in training data collection; (2) Since such artifacts are general existed in DeepFake videos from different sources, our method is more robust compared to others. Our method is evaluated on two sets of DeepFake video datasets for its effectiveness in practice.
Resampling Detection . The artifacts introduced by the DeepFake production pipeline is in essence due to affine transforms to the synthesized face. In the literature of digital media forensics, detecting transforms or the underlying resampling algorithm has been extensively studied, , @cite_26 @cite_28 @cite_20 @cite_22 @cite_3 @cite_21 @cite_12 @cite_1 @cite_27 @cite_23 @cite_10 . However, the performance of these methods are affected by the post-processing steps, such as image video compression, which are not subject to simple modeling. Besides, these methods usually aim to estimate the exact resampling operation from whole images, but for our purpose, a simpler solution can be obtained by just comparing regions of potentially synthesized faces and the rest of the image -- the latter are expected to be free of such artifacts while the existence of such artifacts in the former is a telltale cue for the video being a DeepFake.
@cite_26 @cite_22 @cite_28 @cite_21 @cite_1 @cite_3 @cite_27 @cite_23 @cite_10 @cite_12 @cite_20
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Detecting image resampling in re-compressed images is a very challenging problem. Existing approaches to image resampling detection operate by building pre-selected model to locate periodicities in linear predictor residues. Additionally, if an image was JPEG compressed before resampling, existing techniques detect tampering using the artifacts left by the pre-compression. However, state-of-the-art approaches cannot detect resampling in re-compressed images initially compressed with high quality factor. In this paper, we propose a novel deep learning approach to adaptively learn resampling detection features directly from data. To accomplish this, we use our recently proposed constrained convolutional layer. Through a set of experiments we evaluate the effectiveness of our proposed constrained convolutional neural network (CNN) to detect resampling in re-compressed images. The results of these experiments show that our constrained CNN can accurately detect resampling in re-compressed images in scenarios that previous approaches are unable to detect. This paper presents a method to automatically and efficiently detect face tampering in videos, and particularly focuses on two recent techniques used to generate hyper-realistic forged videos: Deepfake and Face2Face. Traditional image forensics techniques are usually not well suited to videos due to the compression that strongly degrades the data. Thus, this paper follows a deep learning approach and presents two networks, both with a low number of layers to focus on the mesoscopic properties of images. We evaluate those fast networks on both an existing dataset and a dataset we have constituted from online videos. The tests demonstrate a very successful detection rate with more than 98 for Deepfake and 95 for Face2Face. This paper presents a method to automatically and efficiently detect face tampering in videos, and particularly focuses on two recent techniques used to generate hyper-realistic forged videos: Deepfake and Face2Face. Traditional image forensics techniques are usually not well suited to videos due to the compression that strongly degrades the data. Thus, this paper follows a deep learning approach and presents two networks, both with a low number of layers to focus on the mesoscopic properties of images. We evaluate those fast networks on both an existing dataset and a dataset we have constituted from online videos. The tests demonstrate a very successful detection rate with more than 98 for Deepfake and 95 for Face2Face. In this work, we describe a new deep learning based method that can effectively distinguish AI-generated fake videos (referred to as DeepFake videos hereafter) from real videos. Our method is based on the observations that current DeepFake algorithm can only generate images of limited resolutions, which need to be further warped to match the original faces in the source video. Such transforms leave distinctive artifacts in the resulting DeepFake videos, and we show that they can be effectively captured by convolutional neural networks (CNNs). Compared to previous methods which use a large amount of real and DeepFake generated images to train CNN classifier, our method does not need DeepFake generated images as negative training examples since we target the artifacts in affine face warping as the distinctive feature to distinguish real and fake images. The advantages of our method are two-fold: (1) Such artifacts can be simulated directly using simple image processing operations on a image to make it as negative example. Since training a DeepFake model to generate negative examples is time-consuming and resource-demanding, our method saves a plenty of time and resources in training data collection; (2) Since such artifacts are general existed in DeepFake videos from different sources, our method is more robust compared to others. Our method is evaluated on two sets of DeepFake video datasets for its effectiveness in practice.