A comprehensive survey on passive techniques for digital video forgery detection

Shelke, Nitin Arvind; Kasana, Singara Singh

doi:10.1007/s11042-020-09974-4

Cited by 55 publications

(44 citation statements)

References 119 publications

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“…The ever-growing threat of deepfakes and large-scale societal implications have driven the development of deepfake forensics to ascertain the trustworthiness and authenticity of digital media. Different deepfake detection approaches have been proposed to address this challenge [8,9]. Early deepfake detection algorithms were primarily based on hand-crafted features, and visible artifacts, such as inconsistency in head poses [10], eye blinking [11] and face wrapping artifacts [12].…”

Section: Introductionmentioning

confidence: 99%

DFDT: An End-to-End DeepFake Detection Framework Using Vision Transformer

Khormali

Yuan

2022

Applied Sciences

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The ever-growing threat of deepfakes and large-scale societal implications has propelled the development of deepfake forensics to ascertain the trustworthiness of digital media. A common theme of existing detection methods is using Convolutional Neural Networks (CNNs) as a backbone. While CNNs have demonstrated decent performance on learning local discriminative information, they fail to learn relative spatial features and lose important information due to constrained receptive fields. Motivated by the aforementioned challenges, this work presents DFDT, an end-to-end deepfake detection framework that leverages the unique characteristics of transformer models, for learning hidden traces of perturbations from both local image features and global relationship of pixels at different forgery scales. DFDT is specifically designed for deepfake detection tasks consisting of four main components: patch extraction & embedding, multi-stream transformer block, attention-based patch selection followed by a multi-scale classifier. DFDT’s transformer layer benefits from a re-attention mechanism instead of a traditional multi-head self-attention layer. To evaluate the performance of DFDT, a comprehensive set of experiments are conducted on several deepfake forensics benchmarks. Obtained results demonstrated the surpassing detection rate of DFDT, achieving 99.41%, 99.31%, and 81.35% on FaceForensics++, Celeb-DF (V2), and WildDeepfake, respectively. Moreover, DFDT’s excellent cross-dataset & cross-manipulation generalization provides additional strong evidence on its effectiveness.

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Section: Introductionmentioning

confidence: 99%

DFDT: An End-to-End DeepFake Detection Framework Using Vision Transformer

Khormali

Yuan

2022

Applied Sciences

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“…More recently, the advent of deep learning techniques has enhanced the capabilities of image integrity detection and verification, outperforming traditional methods in several image-related tasks, especially in these where anti-forensic tools were used [109], [110], [115]. In the context of video files, we can find surveys on video steganalysis [109], [110], [116], video forgery detection [91], [92], [94], [110], [117], [118], video forensic tools [91], [109], [119], [120], video surveillance analysis [121], [122], and video content authentication [123]. Finally, digital audio forensics has also been studied in [124].…”

Section: Challenge/limitation Referencesmentioning

confidence: 99%

“…Standardized evaluation procedures and benchmarks [90], [94]- [96], [98], [100], [101], [104], [109]- [114], [116], [117], [123] Explore the use of novel AI methods and novel data types [88], [91], [93], [95], [99], [100], [103], [104], [106], [107], [109], [110], [112], [113], [115], [118], [121]- [124] Robust pre-processing and feature extraction [90], [91], [94], [97], [99], [101], [102], [105], [106], [109], [115], [117], [118], [121], [123], [124] Reduce training and data acquisition overheads [89], [91]…”

Section: Challenge/limitation Referencesmentioning

confidence: 99%

Research Trends, Challenges, and Emerging Topics in Digital Forensics: A Review of Reviews

et al. 2022

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This work was supported by the European Commission under the Horizon 2020 Programme (H2020), as part of the projects LOCARD (https://locard.eu) (Grant Agreement no. 832735) and CyberSec4Europe (https://www.cybersec4europe.eu) (Grant Agreement no. 830929). F. Casino was supported by the Beatriu de Pinós programme of the Government of Catalonia (Grant No. 2020 BP 00035). The content of this article does not reflect the official opinion of the European Union. Responsibility for the information and views expressed therein lies entirely with the authors.

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“…This review presented both image and video forgery issues, but the major focus was on highlighting the issues in the image forensic domain, and few aspects related to video forgery forensic are elaborated. Recently, Shelke and Kasana [23] presented a comprehensive survey on passive techniques for video forgery detection based on features, types of forgeries identified, datasets and performance parameters. Pros and cons of different passive forgery detection techniques are elaborated, along with future challenges.…”

Section: Distinction From Other Surveysmentioning

confidence: 99%

Digital Video Tampering Detection and Localization: Review, Representations, Challenges and Algorithm

et al. 2022

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Digital videos are now low-cost, easy to capture and easy to share on social media due to the common feature of video recording in smart phones and digital devices. However, with the advancement of video editing tools, videos can be tampered (forged) easily for propaganda or to gain illegal advantages—ultimately, the authenticity of videos shared on social media cannot be taken for granted. Over the years, significant research has been devoted to developing new techniques for detecting different types of video tampering. In this paper, we offer a detailed review of existing passive video tampering detection techniques in a systematic way. The answers to research questions prepared for this study are also elaborated. The state-of-the-art research work is analyzed extensively, highlighting the pros and cons and commonly used datasets. Limitations of existing video forensic algorithms are discussed, and we conclude with research challenges and future directions.

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A comprehensive survey on passive techniques for digital video forgery detection

Cited by 55 publications

References 119 publications

DFDT: An End-to-End DeepFake Detection Framework Using Vision Transformer

DFDT: An End-to-End DeepFake Detection Framework Using Vision Transformer

Research Trends, Challenges, and Emerging Topics in Digital Forensics: A Review of Reviews

Digital Video Tampering Detection and Localization: Review, Representations, Challenges and Algorithm

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