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Deepfakes, or synthetic audiovisual media developed with the intent to deceive, are growing increasingly prevalent. Existing methods, employed independently as images/patches or jointly as tubelets, have, up to this point, typically focused on spatial or spatiotemporal inconsistencies. However, the evolving nature of deepfakes demands a holistic approach. Inspection of a given multimedia sample with the intent to validate its authenticity, without adding significant computational overhead has, to date, not been fully explored in the literature. In addition, no work has been done on the impact of different inconsistency dimensions in a single framework. This paper tackles the deepfake detection problem holistically. HolisticDFD, a novel, transformer-based, deepfake detection method which is both lightweight and compact, intelligently combines embeddings from the spatial, temporal and spatiotemporal dimensions to separate deepfakes from bonafide videos. The proposed system achieves 0.926 AUC on the DFDC dataset using just 3% of the parameters used by state-ofthe-art detectors. An evaluation against other datasets shows the efficacy of the proposed framework, and an ablation study shows that the performance of the system gradually improves as embeddings with different data representations are combined. An implementation of the proposed model is available at: https://github.com/smileslab/deepfake-detection/. 

By employing generative deep learning techniques, Deepfakes are created with the intent to create mistrust in society, manipulate public opinion and political decisions, and for other malicious purposes such as blackmail, scamming, and even cyberstalking. As realistic deepfake may involve manipulation of either audio or video or both, thus it is important to explore the possibility of detecting deepfakes through the inadequacy of generative algorithms to synchronize audio and visual modalities. Prevailing performant methods, either detect audio or video cues for deepfakes detection while few ensemble the results after predictions on both modalities without inspecting relationship between audio and video cues. Deepfake detection using joint audiovisual representation learning is not explored much. Therefore, this paper proposes a unified multimodal framework, Multimodaltrace, which extracts learned channels from audio and visual modalities, mixes them independently in IntrAmodality Mixer Layer (IAML), processes them jointly in IntErModality Mixer Layers (IEML) from where it is fed to multilabel classification head. Empirical results show the effectiveness of the proposed framework giving state-of-the-art accuracy of 92.9% on the FakeAVCeleb dataset. The cross-dataset evaluation of the proposed framework on World Leaders and Presidential Deepfake Detection Datasets gives an accuracy of 83.61% and 70% respectively. The study also provides insights into how the model focuses on different parts of audio and visual features through integrated gradient analysis 

The prevalence of voice spoofing attacks in today’s digital world has become a critical security concern. Attackers employ various techniques, such as voice conversion (VC) and text-to-speech (TTS), to generate synthetic speech that imitates the victim’s voice and gain access to sensitive information. The recent advances in synthetic speech generation pose a significant threat to modern security systems, while traditional voice authentication methods are incapable of detecting them effectively. To address this issue, a novel solution for logical access (LA)-based synthetic speech detection is proposed in this paper. SpoTNet is an attention-based spoofing transformer network that includes crafted front-end spoofing features and deep attentive features retrieved using the developed logical spoofing transformer encoder (LSTE). The derived attentive features were then processed by the proposed multi-layer spoofing classifier to classify speech samples as bona fide or synthetic. In synthetic speeches produced by the TTS algorithm, the spectral characteristics of the synthetic speech are altered to match the target speaker’s formant frequencies, while in VC attacks, the temporal alignment of the speech segments is manipulated to preserve the target speaker’s prosodic features. By highlighting these observations, this paper targets the prosodic and phonetic-based crafted features, i.e., the Mel-spectrogram, spectral contrast, and spectral envelope, presenting an effective preprocessing pipeline proven to be effective in synthetic speech detection. The proposed solution achieved state-of-the-art performance against eight recent feature fusion methods with lower EER of 0.95% on the ASVspoof-LA dataset, demonstrating its potential to advance the field of speaker identification and improve speaker recognition systems. 

Deepfakes represent the generation of synthetic/fake images or videos using deep neural networks. As the techniques used for the generation of deepfakes are improving, the threats including social media disinformation, defamation, impersonation, and fraud are becoming more prevalent. The existing deepfakes detection models, including those that use convolution neural networks, do not generalize well when subjected to multiple deepfakes generation techniques and cross-corpora setting. Therefore, there is a need for the development of effective and efficient deepfakes detection methods. To explicitly model part-whole hierarchical relationships by using groups of neurons to encode visual entities and learn the relationships between real and fake artifacts, we propose a novel deep learning model efficient-capsule network (E-Cap Net) for classifying the facial images generated through different deepfakes generative techniques. More specifically, we introduce a low-cost max-feature-map (MFM) activation function in each primary capsule of our proposed E-Cap Net. The use of MFM activation enables our E-Cap Net to become light and robust as it suppresses the low activation neurons in each primary capsule. Performance of our approach is evaluated on two standard, largescale and diverse datasets i.e., Diverse Fake Face Dataset (DFFD) and FaceForensics++ (FF++), and also on the World Leaders Dataset (WLRD). Moreover, we also performed a cross-corpora evaluation to show the generalizability of our method for reliable deepfakes detection. The AUC of 99.99% on DFFD, 99.52% on FF++, and 98.31% on WLRD datasets indicate the effectiveness of our method for detecting the manipulated facial images generated via different deepfakes techniques. 

With the advent of automated speaker verifcation (ASV) systems comes an equal and opposite development: malicious actors may seek to use voice spoofng attacks to fool those same systems. Various counter measures have been proposed to detect these spoofing attacks, but current oferings in this arena fall short of a unifed and generalized approach applicable in real-world scenarios. For this reason, defensive measures for ASV systems produced in the last 6-7 years need to be classifed, and qualitative and quantitative comparisons of state-of-the-art (SOTA) counter measures should be performed to assess the efectiveness of these systems against real-world attacks. Hence, in this work, we conduct a review of the literature on spoofng detection using hand-crafted features, deep learning, and end-to-end spoofng countermeasure solutions to detect logical access attacks, such as speech synthesis and voice conversion, and physical access attacks, i.e., replay attacks. Additionally, we review integrated and unifed solutions to voice spoofng evaluation and speaker verifcation, and adversarial and anti-forensic attacks on both voice counter measures and ASV systems. In an extensive experimental analysis, the limitations and challenges of existing spoofng counter measures are presented, the performance of these counter measures on several datasets is reported, and cross-corpus evaluations are performed, something that is nearly absent in the existing literature, in order to assess the generalizability of existing solutions. For the experiments, we employ the ASVspoof2019, ASVspoof2021, and VSDC datasets along with GMM, SVM, CNN, and CNN-GRU classifers. For reproducibility of the results, the code of the testbed can be found at our GitHub Repository (https://github.com/smileslab/Comparative-Analysis-Voice-Spoofing). 

Exponential growth in the use of smart speakers (SS) for the automation of homes, offices, and vehicles has brought a revolution of convenience to our lives. However, these SSs are susceptible to a variety of spoofing attacks, known/seen and unknown/unseen, created using cutting-edge AI generative algorithms. The realistic nature of these powerful attacks is capable of deceiving the automatic speaker verification (ASV) engines of these SSs, resulting in a huge potential for fraud using these devices. This vulnerability highlights the need for the development of effective countermeasures capable of the reliable detection of known and unknown spoofing attacks. This paper presents a novel end-to-end deep learning model, AEXANet, to effectively detect multiple types of physical- and logical-access attacks, both known and unknown. The proposed countermeasure has the ability to learn low-level cues by analyzing raw audio, utilizes a dense convolutional network for the propagation of diversified raw waveform features, and strengthens feature propagation. This system employs a maximum feature map activation function, which improves the performance against unseen spoofing attacks while making the model more efficient, enabling the model to be used for real-time applications. An extensive evaluation of our model was performed on the ASVspoof 2019 PA and LA datasets, along with TTS and VC samples, separately containing both seen and unseen attacks. Moreover, cross corpora evaluation using the ASVspoof 2019 and ASVspoof 2015 datasets was also performed. Experimental results show the reliability of our method for voice spoofing detection. 

Easy access to audio-visual content on social media, combined with the availability of modern tools such as Tensorflow or Keras, and open-source trained models, along with economical computing infrastructure, and the rapid evolution of deep-learning (DL) methods have heralded a new and frightening trend. Particularly, the advent of easily available and ready to use Generative Adversarial Networks (GANs), have made it possible to generate deepfakes media partially or completely fabricated with the intent to deceive to disseminate disinformation and revenge porn, to perpetrate financial frauds and other hoaxes, and to disrupt government functioning. Existing surveys have mainly focused on the detection of deepfake images and videos; this paper provides a comprehensive review and detailed analysis of existing tools and machine learning (ML) based approaches for deepfake generation, and the methodologies used to detect such manipulations in both audio and video. For each category of deepfake, we discuss information related to manipulation approaches, current public datasets, and key standards for the evaluation of the performance of deepfake detection techniques, along with their results. Additionally, we also discuss open challenges and enumerate future directions to guide researchers on issues which need to be considered in order to improve the domains of both deepfake generation and detection. This work is expected to assist readers in understanding how deepfakes are created and detected, along with their current limitations and where future research may lead.