DeepFake Video Detection: A Time-Distributed Approach

Singh, Amritpal; Saimbhi, Amanpreet Singh; Singh, Navjot; Mittal, Mamta

doi:10.1007/s42979-020-00225-9

Cited by 37 publications

(13 citation statements)

References 30 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%

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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“…However, it still suffers from the identical drawback. The equally spaced method applied by Singh et al [29] improved the classification accuracy by strengthening the incoherence of facial data across frames, whereas it has the drawback of bringing redundant information.…”

Section: Experiments On the Effectiveness Of Video Frame Extraction Methodsmentioning

confidence: 99%

Exposing DeepFake Videos Using Attention Based Convolutional LSTM Network

Xia

et al. 2021

Neural Process Lett

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The detection of face tampering in videos created by artificial intelligence techniques (commonly known as the Deep Fakes) has become an important and challenging task in network security defense. In this paper, we propose a novel attention-based deep fake video detection method, which captures the sharp changes in terms of the facial features caused by the composite video. We utilize the convolutional long short-term memory to extract both spatial and temporal information of DeeFake videos. Meanwhile, we apply the attention mechanism to emphasize the specific facial area of each video frame. Finally, we design a decoder to further fusion multiple frames information for more accurate detection results. Experimental results and comparisons with state-of-the-art methods demonstrate that our framework achieves superior performance.

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“…Singh et al [28] introduced a method to identify movies distorted by deepfake efficiently and comprehensively. The authors developed an architecture that used lower-level characteristics in areas of interest and disparities over many frames.…”

Section: B Deepfake Video Detection Implementationmentioning

confidence: 99%

DFFMD: A Deepfake Face Mask Dataset for Infectious Disease Era With Deepfake Detection Algorithms

et al. 2023

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Deepfake is a technology that creates fake images and videos with replaced or synthesized faces. Deepfakes are becoming a concerning social phenomenon, as they can be maliciously used to generate false political news, disseminate dangerous information, falsify electronic evidence, and commit digital harassment and fraud. The ease and accuracy of creating Deepfakes have been bolstered by the popularity of wearing face masks since the beginning of the infectious disease outbreak (2020). Because these masks obstruct defining facial features, fake videos are now even more challenging to identify, increasing the necessity for advanced Deepfake detection technology. The research also creates a real/fake video dataset with face masks because the field lacks the dataset required for detection-model training. The proposed research proposes a Deepfake Face Mask Dataset (DFFMD) based on a novel Inception-ResNet-v2 with preprocessing stages, featurebased, residual connection, and batch normalization. The combination of preprocessing stages, feature-based, residual connection, and batch normalization increases the detection accuracy of deepfake videos in the presence of facemasks, unlike the traditional methods. The study's results compared with existing state-of-the-art methods detect face-mask-Deepfakes with 99.81% accuracy compared to the traditional InceptionResNetV2 and VGG19, whose accuracy is 77.48%, and 99.25%, respectively. Future work should evaluate the accuracy of developing a subsequent experimental work for increased detection of deepfake with facemasks.

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DeepFake Video Detection: A Time-Distributed Approach

Cited by 37 publications

References 30 publications

Deepfake Detection: A Systematic Literature Review

Deepfake Detection: A Systematic Literature Review

Exposing DeepFake Videos Using Attention Based Convolutional LSTM Network

DFFMD: A Deepfake Face Mask Dataset for Infectious Disease Era With Deepfake Detection Algorithms

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