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.
more »
« less
Audio deepfakes: A survey
A deepfake is content or material that is synthetically generated or manipulated using artificial intelligence (AI) methods, to be passed off as real and can include audio, video, image, and text synthesis. The key difference between manual editing and deepfakes is that deepfakes are AI generated or AI manipulated and closely resemble authentic artifacts. In some cases, deepfakes can be fabricated using AI-generated content in its entirety. Deepfakes have started to have a major impact on society with more generation mechanisms emerging everyday. This article makes a contribution in understanding the landscape of deepfakes, and their detection and generation methods. We evaluate various categories of deepfakes especially in audio. The purpose of this survey is to provide readers with a deeper understanding of (1) different deepfake categories; (2) how they could be created and detected; (3) more specifically, how audio deepfakes are created and detected in more detail, which is the main focus of this paper. We found that generative adversarial networks (GANs), convolutional neural networks (CNNs), and deep neural networks (DNNs) are common ways of creating and detecting deepfakes. In our evaluation of over 150 methods, we found that the majority of the focus is on video deepfakes, and, in particular, the generation of video deepfakes. We found that for text deepfakes, there are more generation methods but very few robust methods for detection, including fake news detection, which has become a controversial area of research because of the potential heavy overlaps with human generation of fake content. Our study reveals a clear need to research audio deepfakes and particularly detection of audio deepfakes. This survey has been conducted with a different perspective, compared to existing survey papers that mostly focus on just video and image deepfakes. This survey mainly focuses on audio deepfakes that are overlooked in most of the existing surveys. This article's most important contribution is to critically analyze and provide a unique source of audio deepfake research, mostly ranging from 2016 to 2021. To the best of our knowledge, this is the first survey focusing on audio deepfakes generation and detection in English.
more »
« less
- Award ID(s):
- 2210011
- NSF-PAR ID:
- 10415111
- Date Published:
- Journal Name:
- Frontiers in Big Data
- Volume:
- 5
- ISSN:
- 2624-909X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
With the fast development of Fifth-/Sixth-Generation (5G/6G) communications and the Internet of Video Things (IoVT), a broad range of mega-scale data applications emerge (e.g., all-weather all-time video). These network-based applications highly depend on reliable, secure, and real-time audio and/or video streams (AVSs), which consequently become a target for attackers. While modern Artificial Intelligence (AI) technology is integrated with many multimedia applications to help enhance its applications, the development of General Adversarial Networks (GANs) also leads to deepfake attacks that enable manipulation of audio or video streams to mimic any targeted person. Deepfake attacks are highly disturbing and can mislead the public, raising further challenges in policy, technology, social, and legal aspects. Instead of engaging in an endless AI arms race “fighting fire with fire”, where new Deep Learning (DL) algorithms keep making fake AVS more realistic, this paper proposes a novel approach that tackles the challenging problem of detecting deepfaked AVS data leveraging Electrical Network Frequency (ENF) signals embedded in the AVS data as a fingerprint. Under low Signal-to-Noise Ratio (SNR) conditions, Short-Time Fourier Transform (STFT) and Multiple Signal Classification (MUSIC) spectrum estimation techniques are investigated to detect the Instantaneous Frequency (IF) of interest. For reliable authentication, we enhanced the ENF signal embedded through an artificial power source in a noisy environment using the spectral combination technique and a Robust Filtering Algorithm (RFA). The proposed signal estimation workflow was deployed on a continuous audio/video input for resilience against frame manipulation attacks. A Singular Spectrum Analysis (SSA) approach was selected to minimize the false positive rate of signal correlations. Extensive experimental analysis for a reliable ENF edge-based estimation in deepfaked multimedia recordings is provided to facilitate the need for distinguishing artificially altered media content.more » « less
-
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.more » « less
-
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 analysismore » « less
-
We present an innovative approach to auto-annotate Expert Defined Linguistic Features (EDLFs) as subsequences in audio time series to improve audio deepfake discernment. In our prior work, these linguistic features – namely pitch, pause, breath, consonant release bursts, and overall audio quality, labeled by experts on the entire audio signal – have been shown to improve detection of audio deepfakes with AI algorithms. We now expand our approach to pilot a way to auto annotate subsequences in the time series that correspond to each EDLF. We developed an ensemble of discords, i.e. anomalies in time series, detected using matrix profiles across multiple discord lengths to identify multiple types of EDLFs. Working closely with linguistic experts, we evaluated where discords overlapped with EDLFs in the audio signal data. Our ensemble method to detect discords across multiple discord lengths achieves much higher accuracy than using individual discord lengths to detect EDLFs. With this approach and domain validation we establish the feasibility of using time series subsequences to capture EDLFs to supplement annotation by domain experts, for improved audio deepfake detection.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fdata.2022.1001063
