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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.
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E-Cap Net: an efficient-capsule network for shallow and deepfakes forgery detection
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.
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- Award ID(s):
- 1815724
- PAR ID:
- 10417496
- Date Published:
- Journal Name:
- Multimedia Systems
- ISSN:
- 0942-4962
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1007/s00530-023-01092-z
