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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2401.06999 |
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| _version_ | 1866929208722194432 |
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| author | Yadav, Ankit Vishwakarma, Dinesh Kumar |
| author_facet | Yadav, Ankit Vishwakarma, Dinesh Kumar |
| contents | With the large chunks of social media data being created daily and the parallel rise of realistic multimedia tampering methods, detecting and localising tampering in images and videos has become essential. This survey focusses on approaches for tampering detection in multimedia data using deep learning models. Specifically, it presents a detailed analysis of benchmark datasets for malicious manipulation detection that are publicly available. It also offers a comprehensive list of tampering clues and commonly used deep learning architectures. Next, it discusses the current state-of-the-art tampering detection methods, categorizing them into meaningful types such as deepfake detection methods, splice tampering detection methods, copy-move tampering detection methods, etc. and discussing their strengths and weaknesses. Top results achieved on benchmark datasets, comparison of deep learning approaches against traditional methods and critical insights from the recent tampering detection methods are also discussed. Lastly, the research gaps, future direction and conclusion are discussed to provide an in-depth understanding of the tampering detection research arena. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_06999 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Datasets, Clues and State-of-the-Arts for Multimedia Forensics: An Extensive Review Yadav, Ankit Vishwakarma, Dinesh Kumar Computer Vision and Pattern Recognition With the large chunks of social media data being created daily and the parallel rise of realistic multimedia tampering methods, detecting and localising tampering in images and videos has become essential. This survey focusses on approaches for tampering detection in multimedia data using deep learning models. Specifically, it presents a detailed analysis of benchmark datasets for malicious manipulation detection that are publicly available. It also offers a comprehensive list of tampering clues and commonly used deep learning architectures. Next, it discusses the current state-of-the-art tampering detection methods, categorizing them into meaningful types such as deepfake detection methods, splice tampering detection methods, copy-move tampering detection methods, etc. and discussing their strengths and weaknesses. Top results achieved on benchmark datasets, comparison of deep learning approaches against traditional methods and critical insights from the recent tampering detection methods are also discussed. Lastly, the research gaps, future direction and conclusion are discussed to provide an in-depth understanding of the tampering detection research arena. |
| title | Datasets, Clues and State-of-the-Arts for Multimedia Forensics: An Extensive Review |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.06999 |