Combating Deepfakes: Multi-LSTM and Blockchain as Proof of Authenticity for Digital Media

Chan, Christopher Chun Ki; Kumar, Vimal; Delaney, Steven; Gochoo, Munkhjargal

doi:10.1109/ai4g50087.2020.9311067

Cited by 20 publications

(15 citation statements)

References 35 publications

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“…decentralized storage ability to Blockchain-based Ethereum Name service. Chan et al [115] proposed a decentralized approach based on Blockchain to trace and track digital content's historical provenance (i.e., image, videos, etc.). In this proposed approach, multiple LSTM networks are being used as a deep encoder for creating discriminating features, which are then compressed and used to hash the transaction.…”

Section: • Integrates the Critical Features Of Ipfs [114]-basedmentioning

confidence: 99%

Deepfake Detection: A Systematic Literature Review

et al. 2022

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Over the last few decades, rapid progress in AI, machine learning, and deep learning has resulted in new techniques and various tools for manipulating multimedia. Though the technology has been mostly used in legitimate applications such as for entertainment and education, etc., malicious users have also exploited them for unlawful or nefarious purposes. For example, high-quality and realistic fake videos, images, or audios have been created to spread misinformation and propaganda, foment political discord and hate, or even harass and blackmail people. The manipulated, high-quality and realistic videos have become known recently as Deepfake. Various approaches have since been described in the literature to deal with the problems raised by Deepfake. To provide an updated overview of the research works in Deepfake detection, we conduct a systematic literature review (SLR) in this paper, summarizing 112 relevant articles from 2018 to 2020 that presented a variety of methodologies. We analyze them by grouping them into four different categories: deep learning-based techniques, classical machine learning-based methods, statistical techniques, and blockchain-based techniques. We also evaluate the performance of the detection capability of the various methods with respect to different datasets and conclude that the deep learning-based methods outperform other methods in Deepfake detection.

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Section: • Integrates the Critical Features Of Ipfs [114]-basedmentioning

confidence: 99%

Deepfake Detection: A Systematic Literature Review

et al. 2022

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“…Prevention: For the prevention of deepfakes, some suggested blockchains and other distributed ledger technologies (DLTs) can be used for finding data provenance and tracing the information (Chauhan and Kumar, 2020 ; Fraga-Lamas and Fernandez-Carames, 2020 ; Ki Chan et al, 2020 ; Yazdinejad et al, 2020 ). Extracting and comparing affecting cues corresponding to perceived emotions from the digital content is also proposed as a way to combat deepfakes (Mittal et al, 2020 ).…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

Audio deepfakes: A survey

Khanjani

Watson²,

Janeja³

2023

Front. Big Data

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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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“…For instance, Koopman et al [ 62 ] analyzed photo response non-uniformity (PRNU) for detection. Also, warping artifacts [ 63 ], eye blinking [ 64 ], optical flow with CNNs [ 65 ], heart rate [ 66 ], image quality [ 28 ], local image textures [ 37 ], long short-term memory (LSTM) and recurrent neural network (RNN) [ 67 ], multi-LSTM and blockchain [ 68 ], clustering [ 69 ], context [ 70 ], compression artifacts [ 71 ], metric learning [ 72 ], CNN ensemble [ 73 ], Identity-aware [ 74 ], transformers [ 75 ], audio-visual dissonance [ 76 ], and multi-attentional [ 77 ] features were used. Very few works have been focused on deepfake detection method’s explainability (e.g., [ 78 ]) and generalization capability (e.g., work of Bekci et al in [ 38 ] and Aneja et al [ 79 ] work using zero-shot learning).…”

Section: Deepfake Generation and Detectionmentioning

confidence: 99%

Deepfakes Generation and Detection: A Short Survey

Akhtar

2023

J. Imaging

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Advancements in deep learning techniques and the availability of free, large databases have made it possible, even for non-technical people, to either manipulate or generate realistic facial samples for both benign and malicious purposes. DeepFakes refer to face multimedia content, which has been digitally altered or synthetically created using deep neural networks. The paper first outlines the readily available face editing apps and the vulnerability (or performance degradation) of face recognition systems under various face manipulations. Next, this survey presents an overview of the techniques and works that have been carried out in recent years for deepfake and face manipulations. Especially, four kinds of deepfake or face manipulations are reviewed, i.e., identity swap, face reenactment, attribute manipulation, and entire face synthesis. For each category, deepfake or face manipulation generation methods as well as those manipulation detection methods are detailed. Despite significant progress based on traditional and advanced computer vision, artificial intelligence, and physics, there is still a huge arms race surging up between attackers/offenders/adversaries (i.e., DeepFake generation methods) and defenders (i.e., DeepFake detection methods). Thus, open challenges and potential research directions are also discussed. This paper is expected to aid the readers in comprehending deepfake generation and detection mechanisms, together with open issues and future directions.

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Combating Deepfakes: Multi-LSTM and Blockchain as Proof of Authenticity for Digital Media

Cited by 20 publications

References 35 publications

Deepfake Detection: A Systematic Literature Review

Deepfake Detection: A Systematic Literature Review

Audio deepfakes: A survey

Deepfakes Generation and Detection: A Short Survey

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