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.
The unique stature of photographs as a definitive recording of events is being diminished due, in part, to the ease with which digital images can be manipulated and altered. Although good forgeries may leave no visual clues of having been tampered with, they may, nevertheless, alter the underlying statistics of an image. For example, we describe how resampling (e.g., scaling or rotating) introduces specific statistical correlations, and describe how these correlations can be automatically detected in any portion of an image. This technique works in the absence of any digital watermark or signature. We show the efficacy of this approach on uncompressed TIFF images, and JPEG and GIF images with minimal compression. We expect this technique to be among the first of many tools that will be needed to expose digital forgeries. This paper revisits the state-of-the-art resampling detector, which is based on periodic artifacts in the residue of a local linear predictor. Inspired by recent findings from the literature, we take a closer look at the complex detection procedure and model the detected artifacts in the spatial and frequency domain by means of the variance of the prediction residue. We give an exact formulation on how transformation parameters influence the appearance of periodic artifacts and analytically derive the expected position of characteristic resampling peaks. We present an equivalent accelerated and simplified detector, which is orders of magnitudes faster than the conventional scheme and experimentally shown to be comparably reliable. Usually digital image forgeries are created by copy-pasting a portion of an image onto some other image. While doing so, it is often necessary to resize the pasted portion of the image to suit the sampling grid of the host image. The resampling operation changes certain characteristics of the pasted portion, which when detected serves as a clue of tampering. In this paper, we present deterministic techniques to detect resampling, and localize the portion of the image that has been tampered with. Two of the techniques are in pixel domain and two others in frequency domain. We study the efficacy of our techniques against JPEG compression and subsequent resampling of the entire tampered image. Resampling detection has become a standard tool in digital image forensics. This paper investigates the important case of resampling detection in re-compressed JPEG images. We show how blocking artifacts of the previous compression step can help to increase the otherwise drastically reduced detection performance in JPEG compressed images. We give a formulation on how affine transformations of JPEG compressed images affect state-of-the-art resampling detectors and derive a new efficient detection variant, which better suits this relevant detection scenario. The principal appropriateness of using JPEG pre-compression artifacts for the detection of resampling in re-compressed images is backed with experimental evidence on a large image set and for a variety of different JPEG qualities. To create convincing forged images, manipulated images or parts of them are usually exposed to some geometric operations which require a resampling step. Therefore, detecting traces of resampling became an important approach in the field of image forensics. In this paper, we revisit existing techniques for resampling detection and design some targeted attacks in order to assess their reliability. We show that the combination of multiple resampling and hybrid median filtering works well for hiding traces of resampling. Moreover, we propose an improved technique for detecting resampling using image forensic tools. Experimental evaluations show that the proposed technique is good for resampling detection and more robust against some targeted attacks. Resampling detection has become a standard tool for forensic analyses of digital images. This paper presents new variants of image transformation operations which are undetectable by resampling detectors based on periodic variations in the residual signal of local linear predictors in the spatial domain. The effectiveness of the proposed method is supported with evidence from experiments on a large image database for various parameter settings. We benchmark detectability as well as the resulting image quality against conventional linear and bicubic interpolation and interpolation with a sinc kernel. These early findings on ldquocounter-forensicrdquo techniques put into question the reliability of known forensic tools against smart counterfeiters in general, and might serve as benchmarks and motivation for the development of much improved forensic techniques. Millions of photos are uploaded to social networking sites every day. However, the authenticity of these images has been severely questioned. Recent advances in image editing software have helped forged images spread widely. While these fake images leave no visual clues, they can still be detected by inspecting the traces left by the resampling process. In this paper, we propose a novel rotation-tolerant resampling detection method, and design a blind image forgery detection algorithm based on this resampling detection method. A measurement called "Rate-Distance" is devised for measuring the distance between two resampled images with different resampling history. Images are classified based on their "Rate-Distances". Through experimental results, we demonstrate that the proposed method can achieve high detection accuracy. This study presents a method for resampling detection. By combining texture analysis with resampling detection, the task of resampling detection is considered as a texture classification problem. In other words, the influence of resampling operations on a raw single-sampled image is viewed as an alteration of the image texture in a fine scale. First, local linear transform is used to obtain textural detail sub-bands. A 36-D feature vector is then extracted from the normalized characteristic function moments of textural detail sub-bands to train a support vector machine classifier. Finally, experimental results are reported on three databases, with each having almost 10,000 images. Comparison with the previous study reveals that the proposed method is effective for resampling detection. In addition, extensive experiments on cover and stego bitmap images illustrate that the proposed method is essential for constructing accurate targeted and blind steganalysis methods for heterogeneous images, raw single-sampled images, and images resampled at different scales. Resampling is an important signature of manipulated images. In this paper, we propose two methods to detect and localize image manipulations based on a combination of resampling features and deep learning. In the first method, the Radon transform of resampling features are computed on overlapping image patches. Deep learning classifiers and a Gaussian conditional random field model are then used to create a heatmap. Tampered regions are located using a Random Walker segmentation method. In the second method, resampling features computed on overlapping image patches are passed through a Long short-term memory (LSTM) based network for classification and localization. We compare the performance of detection localization of both these methods. Our experimental results show that both techniques are effective in detecting and localizing digital image forgeries. Detection of resampling traces for digital image blind authentication has been addressed recently by A. C. Gallagher and later extended by B. Mahdian and S. Saic. On the other side, it is well known from the synchronization area in communications that prefiltering is an appropriate tool to improve the performance of those schemes exploiting the underlying cyclostationarity of communication signals. Thus, the detection of resampling manipulations improves significantly when the derivative of the interpolated signal is used for covariance computation. This work focuses on the role of prefiltering as a way of boosting resampling traces and, in particular, on the use of derivation. In this paper, we analyze and analytically describe the specific statistical changes brought into the covariance structure of signal by the interpolation process. We show that interpolated signals and their derivatives contain specific detectable periodic properties. Based on this, we propose a blind, efficient, and automatic method capable of finding traces of resampling and interpolation. The proposed method can be very useful in many areas, especially in image security and authentication. For instance, when two or more images are spliced together, to create high quality and consistent image forgeries, almost always geometric transformations, such as scaling, rotation, or skewing are needed. These procedures are typically based on a resampling and interpolation step. By having a method capable of detecting the traces of resampling, we can significantly reduce the successful usage of such forgeries. Among other points, the presented method is also very useful in estimation of the geometric transformations factors.