Swapped face detection using deep learning and subjective assessment

Ding, Xinyi; Raziei, Zohreh; Larson, Eric C.; Olinick, Eli V.; Krueger, Paul S.; Hahsler, Michael

doi:10.1186/s13635-020-00109-8

Cited by 44 publications

(13 citation statements)

References 20 publications

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“…• The information is stored in a permission-based Blockchain, which gives the owner control over its contents. Based on the studies, taking together all these methods, Table 3 [11], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77], [78], [79], [80], [81], [82], [83], [84], [86],…”

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

confidence: 99%

“…Recent research applied deep learning-based approaches, especially the CNN models, to learn how to mechanically or directly learn perceptible and selective features to identify such Deepfake. For example, Ding et al [82] introduced a two-phase CNN method for Deepfake detection. The first stage extracts particular features among counterfeit and actual images by incorporating various dense units, where each of them includes a list of dense blocks that are forged images.…”

Section: Observationsmentioning

confidence: 99%

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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%

Section: Observationsmentioning

confidence: 99%

Deepfake Detection: A Systematic Literature Review

et al. 2022

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“…Dang et al [ 161 ] investigated the use of attention mechanism for the detection and segmentation of tampered-with faces. In [ 162 ], authors used deep transfer learning for face swapping detection. Hsu et al [ 163 ] made use of a so-called Common Fake Feature Network (CFFN) consisting of several dense units and a Siamese network for Deepfake detection.…”

Section: Other Specific Forensic Problemsmentioning

confidence: 99%

A Comprehensive Review of Deep-Learning-Based Methods for Image Forensics

Camacho

Wang

2021

J. Imaging

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Seeing is not believing anymore. Different techniques have brought to our fingertips the ability to modify an image. As the difficulty of using such techniques decreases, lowering the necessity of specialized knowledge has been the focus for companies who create and sell these tools. Furthermore, image forgeries are presently so realistic that it becomes difficult for the naked eye to differentiate between fake and real media. This can bring different problems, from misleading public opinion to the usage of doctored proof in court. For these reasons, it is important to have tools that can help us discern the truth. This paper presents a comprehensive literature review of the image forensics techniques with a special focus on deep-learning-based methods. In this review, we cover a broad range of image forensics problems including the detection of routine image manipulations, detection of intentional image falsifications, camera identification, classification of computer graphics images and detection of emerging Deepfake images. With this review it can be observed that even if image forgeries are becoming easy to create, there are several options to detect each kind of them. A review of different image databases and an overview of anti-forensic methods are also presented. Finally, we suggest some future working directions that the research community could consider to tackle in a more effective way the spread of doctored images.

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“…Ding et al [45] proposed deep learning based face detection and assessment with swapped nature. The pairwise comparison of the human subjects are collected in a website.…”

Section: Review About the Face Detection Approaches Using Ml/ Deep Learning Algorithmsmentioning

confidence: 99%

Study Of Deep Learning Techniques For Differently Abled Applications And Detection Techniques

Anandh¹

2021

TURCOMAT

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In worldwide, Visually Impaired Persons (VIP) are facing several issue related to visual impairment and blindness and they are assisted with technical inventions. Based on the survey from World Health Organization (WHO), around 2.2 billion peoples are suffered from visual impairment and among them 1 million peoples are suffered by blindness. Vision is the major sensing organ of the human and to assist the VIP regarding this, there are various digital vision products are in the market which is based on digital technologies and advanced algorithms. This will transform the VIP’s vision world into audio to get to know about their surroundings includes objects, motion, obstacles and spatial locations. The objective of this paper is to provide the detailed survey about the existing object recognition, face recognition and text to voice recognition methods proposed to assist VIP. Due to the increase development of machine learning and deep learning algorithms, the digital image recognition and object recognition are become more efficient. These advanced technologies can make sure to assist the VIP to detect and recognize the people face and objects in front of them in the form of audio that are practiced by them on daily basis.

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Swapped face detection using deep learning and subjective assessment

Cited by 44 publications

References 20 publications

Deepfake Detection: A Systematic Literature Review

Deepfake Detection: A Systematic Literature Review

A Comprehensive Review of Deep-Learning-Based Methods for Image Forensics

Study Of Deep Learning Techniques For Differently Abled Applications And Detection Techniques

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