ResViT: A Framework for Deepfake Videos Detection

Deepfake makes it quite easy to synthesize videos or images using deep learning techniques, which leads to substantial danger and worry for most of the world\u27s renowned people. Spreading false news or synthesizing one\u27s video or image can harm people and their lack of trust on social and electronic media. To efficiently identify deepfake images, we propose ResViT, which uses the ResNet model for feature extraction, while the vision transformer is used for classification. The ResViT architecture uses the feature extractor to extract features from the images of the videos, which are used to classify the input as fake or real. Moreover, the ResViT architectures focus equally on data pre-processing, as it improves performance. We conducted extensive experiments on the five mostly used datasets our results with the baseline model on the following datasets of Celeb-DF, Celeb-DFv2, FaceForensics++, FF-Deepfake Detection, and DFDC2. Our analysis revealed that ResViT performed better than the baseline and achieved the prediction accuracy of 80.48%, 87.23%, 75.62%, 78.45%, and 84.55% on Celeb-DF, Celeb-DFv2, FaceForensics++, FF-Deepfake Detection, and DFDC2 datasets, respectively

GANprintR: Improved Fakes and Evaluation of the State of the Art in Face Manipulation Detection

© 2020 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThe availability of large-scale facial databases, together with the remarkable progresses of deep learning technologies, in particular Generative Adversarial Networks (GANs), have led to the generation of extremely realistic fake facial content, raising obvious concerns about the potential for misuse. Such concerns have fostered the research on manipulation detection methods that, contrary to humans, have already achieved astonishing results in various scenarios. In this study, we focus on the synthesis of entire facial images, which is a specific type of facial manipulation. The main contributions of this study are four-fold: i) a novel strategy to remove GAN 'fingerprints' from synthetic fake images based on autoencoders is described, in order to spoof facial manipulation detection systems while keeping the visual quality of the resulting images; ii) an in-depth analysis of the recent literature in facial manipulation detection; iii) a complete experimental assessment of this type of facial manipulation, considering the state-of-the-art fake detection systems (based on holistic deep networks, steganalysis, and local artifacts), remarking how challenging is this task in unconstrained scenarios; and finally iv) we announce a novel public database, named iFakeFaceDB, yielding from the application of our proposed GAN-fingerprint Removal approach (GANprintR) to already very realistic synthetic fake images. The results obtained in our empirical evaluation show that additional efforts are required to develop robust facial manipulation detection systems against unseen conditions and spoof techniques, such as the one proposed in this studyThis work has been supported by projects: PRIMA (H2020-MSCA-ITN-2019-860315), TRESPASS-ETN (H2020-MSCA-ITN2019-860813), BIBECA (RTI2018-101248-B-I00 MINECO/FEDER), BioGuard (Ayudas Fundación BBVA a Equipos de Investigación Cientíifica 2017), Accenture, by NOVA LINCS (UIDB/04516/2020) with the financial support of FCT - Fundação para a Ciência e a Tecnologia, through national funds, and by FCT/MCTES through national funds and co-funded by EU under the project UIDB/EEA/50008/202

Voice Spoofing Countermeasures: Taxonomy, State-of-the-art, experimental analysis of generalizability, open challenges, and the way forward

Malicious actors may seek to use different voice-spoofing attacks to fool ASV systems and even use them for spreading misinformation. Various countermeasures have been proposed to detect these spoofing attacks. Due to the extensive work done on spoofing detection in automated speaker verification (ASV) systems in the last 6-7 years, there is a need to classify the research and perform qualitative and quantitative comparisons on state-of-the-art countermeasures. Additionally, no existing survey paper has reviewed integrated solutions to voice spoofing evaluation and speaker verification, adversarial/antiforensics attacks on spoofing countermeasures, and ASV itself, or unified solutions to detect multiple attacks using a single model. Further, no work has been done to provide an apples-to-apples comparison of published countermeasures in order to assess their generalizability by evaluating them across corpora. In this work, we conduct a review of the literature on spoofing detection using hand-crafted features, deep learning, end-to-end, and universal spoofing countermeasure solutions to detect speech synthesis (SS), voice conversion (VC), and replay attacks. Additionally, we also review integrated solutions to voice spoofing evaluation and speaker verification, adversarial and anti-forensics attacks on voice countermeasures, and ASV. The limitations and challenges of the existing spoofing countermeasures are also presented. We report the performance of these countermeasures on several datasets and evaluate them across corpora. For the experiments, we employ the ASVspoof2019 and VSDC datasets along with GMM, SVM, CNN, and CNN-GRU classifiers. (For reproduceability of the results, the code of the test bed can be found in our GitHub Repository

Secure Automatic Speaker Verification Systems

The growing number of voice-enabled devices and applications consider automatic speaker verification (ASV) a fundamental component. However, maximum outreach for ASV in critical domains e.g., financial services and health care, is not possible unless we overcome security breaches caused by voice cloning, and replayed audios collectively known as the spoofing attacks. The audio spoofing attacks over ASV systems on one hand strictly limit the usability of voice-enabled applications; and on the other hand, the counterfeiter also remains untraceable. Therefore, to overcome these vulnerabilities, a secure ASV (SASV) system is presented in this dissertation. The proposed SASV system is based on the concept of novel sign modified acoustic local ternary pattern (sm-ALTP) features and asymmetric bagging-based classifier-ensemble. The proposed audio representation approach clusters the high and low-frequency components in audio frames by normally distributing frequency components against a convex function. Then, the neighborhood statistics are applied to capture the user specific vocal tract information. This information is then utilized by the classifier ensemble that is based on the concept of weighted normalized voting rule to detect various spoofing attacks. Contrary to the existing ASV systems, the proposed SASV system not only detects the conventional spoofing attacks (i.e. voice cloning, and replays), but also the new attacks that are still unexplored by the research community and a requirement of the future. In this regard, a concept of cloned replays is presented in this dissertation, where, replayed audios contains the microphone characteristics as well as the voice cloning artifacts. This depicts the scenario when voice cloning is applied in real-time. The voice cloning artifacts suppresses the microphone characteristics thus fails replay detection modules and similarly with the amalgamation of microphone characteristics the voice cloning detection gets deceived. Furthermore, the proposed scheme can be utilized to obtain a possible clue against the counterfeiter through voice cloning algorithm detection module that is also a novel concept proposed in this dissertation. The voice cloning algorithm detection module determines the voice cloning algorithm used to generate the fake audios. Overall, the proposed SASV system simultaneously verifies the bonafide speakers and detects the voice cloning attack, cloning algorithm used to synthesize cloned audio (in the defined settings), and voice-replay attacks over the ASVspoof 2019 dataset. In addition, the proposed method detects the voice replay and cloned voice replay attacks over the VSDC dataset. Rigorous experimentation against state-of-the-art approaches also confirms the robustness of the proposed research