As image tampering becomes ever more sophisticated and commonplace, the need for image forensics algorithms that can accurately and quickly detect forgeries grows. In this paper, we revisit the ideas of image querying and retrieval to provide clues to better localize forgeries. We propose a method to perform large-scale image forensics on the order of one million images using the help of an image search algorithm and database to gather contextual clues as to where tampering may have taken place. In this vein, we introduce five new strongly invariant image comparison methods and test their effectiveness under heavy noise, rotation, and color space changes. Lastly, we show the effectiveness of these methods compared to passive image forensics using Nimble [this https URL], a new, state-of-the-art dataset from the National Institute of Standards and Technology (NIST).
By always considering a scenario with multiple images, sophisticated methods for patch comparison using deep learning have been proposed @cite_6 @cite_26 . However, the corresponding models were trained for highly specific keypoint matching scenarios. Techniques like these do not capture the variations present in realistic forgeries, and thus cannot be used as-is in a real-world forensic scenario.
@cite_26 @cite_6
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We propose a new image forgery detection technique which fuses the outputs of two very diverse tools, based on machine learning and block-matching, respectively. The machine-learning tool builds upon some local descriptors recently proposed in the steganalysis field, which are selected and merged based on an ad hoc measure of reliability. The block-matching tool leverages on the patchmatch algorithm for fast search of candidate matchings. Both tools are fine-tuned so as to optimize their fusion which, in turn, exploits the respective strengths and weaknesses of each tool. The proposed technique ranked first in phase 1 of the first Image Forensics Challenge organized in 2013 by the IEEE Signal Processing Society. Over the past decade, many efforts have been made in passive image forensics. Although it is able to detect tampered images at high accuracies based on some carefully designed mechanisms, localization of the tampered regions in a fake image still presents many challenges, especially when the type of tampering operation is unknown. Some researchers have realized that it is necessary to integrate different forensic approaches in order to obtain better localization performance. However, several important issues have not been comprehensively studied, for example, how to select and improve readjust proper forensic approaches, and how to fuse the detection results of different forensic approaches to obtain good localization results. In this paper, we propose a framework to improve the performance of forgery localization via integrating tampering possibility maps. In the proposed framework, we first select and improve two existing forensic approaches, i.e., statistical feature-based detector and copy-move forgery detector, and then adjust their results to obtain tampering possibility maps. After investigating the properties of possibility maps and comparing various fusion schemes, we finally propose a simple yet very effective strategy to integrate the tampering possibility maps to obtain the final localization results. The extensive experiments show that the two improved approaches used in our framework significantly outperform the state-of-the-art techniques, and the proposed fusion results achieve the best @math -score in the IEEE IFS-TC Image Forensics Challenge. When creating a forgery, a forger can modify an image using many different image editing operations. Since a forensic examiner must test for each of these, significant interest has arisen in the development of universal forensic algorithms capable of detecting many different image editing operations and manipulations. In this paper, we propose a universal forensic approach to performing manipulation detection using deep learning. Specifically, we propose a new convolutional network architecture capable of automatically learning manipulation detection features directly from training data. In their current form, convolutional neural networks will learn features that capture an image's content as opposed to manipulation detection features. To overcome this issue, we develop a new form of convolutional layer that is specifically designed to suppress an image's content and adaptively learn manipulation detection features. Through a series of experiments, we demonstrate that our proposed approach can automatically learn how to detect multiple image manipulations without relying on pre-selected features or any preprocessing. The results of these experiments show that our proposed approach can automatically detect several different manipulations with an average accuracy of 99.10 . 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.