Mixture-of-Noises Enhanced Forgery-Aware Predictor for Multi-Face Manipulation Detection and Localization Article Swipe
Changtao Miao
,
Qi Chu
,
Tao Gong
,
Zhentao Tan
,
Zhenchao Jin
,
Wanyi Zhuang
,
Man Luo
,
Honggang Hu
,
Nenghai Yu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2408.02306
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2408.02306
With the advancement of face manipulation technology, forgery images in multi-face scenarios are gradually becoming a more complex and realistic challenge. Despite this, detection and localization methods for such multi-face manipulations remain underdeveloped. Traditional manipulation localization methods either indirectly derive detection results from localization masks, resulting in limited detection performance, or employ a naive two-branch structure to simultaneously obtain detection and localization results, which cannot effectively benefit the localization capability due to limited interaction between two tasks. This paper proposes a new framework, namely MoNFAP, specifically tailored for multi-face manipulation detection and localization. The MoNFAP primarily introduces two novel modules: the Forgery-aware Unified Predictor (FUP) Module and the Mixture-of-Noises Module (MNM). The FUP integrates detection and localization tasks using a token learning strategy and multiple forgery-aware transformers, which facilitates the use of classification information to enhance localization capability. Besides, motivated by the crucial role of noise information in forgery detection, the MNM leverages multiple noise extractors based on the concept of the mixture of experts to enhance the general RGB features, further boosting the performance of our framework. Finally, we establish a comprehensive benchmark for multi-face detection and localization and the proposed \textit{MoNFAP} achieves significant performance. The codes will be made available.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2408.02306
- https://arxiv.org/pdf/2408.02306
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403369869
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4403369869Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2408.02306Digital Object Identifier
- Title
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Mixture-of-Noises Enhanced Forgery-Aware Predictor for Multi-Face Manipulation Detection and LocalizationWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-08-05Full publication date if available
- Authors
-
Changtao Miao, Qi Chu, Tao Gong, Zhentao Tan, Zhenchao Jin, Wanyi Zhuang, Man Luo, Honggang Hu, Nenghai YuList of authors in order
- Landing page
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https://arxiv.org/abs/2408.02306Publisher landing page
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https://arxiv.org/pdf/2408.02306Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2408.02306Direct OA link when available
- Concepts
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Face (sociological concept), Artificial intelligence, Computer science, Face detection, Computer vision, Pattern recognition (psychology), Facial recognition system, Sociology, Social scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.capability. | 137 |
| abstract_inverted_index.effectively | 65 |
| abstract_inverted_index.facilitates | 128 |
| abstract_inverted_index.information | 133, 146 |
| abstract_inverted_index.interaction | 73 |
| abstract_inverted_index.performance | 174 |
| abstract_inverted_index.significant | 194 |
| abstract_inverted_index.technology, | 6 |
| abstract_inverted_index.localization | 25, 35, 43, 61, 68, 116, 136, 188 |
| abstract_inverted_index.manipulation | 5, 34, 89 |
| abstract_inverted_index.performance, | 49 |
| abstract_inverted_index.performance. | 195 |
| abstract_inverted_index.specifically | 85 |
| abstract_inverted_index.Forgery-aware | 101 |
| abstract_inverted_index.comprehensive | 182 |
| abstract_inverted_index.forgery-aware | 125 |
| abstract_inverted_index.localization. | 92 |
| abstract_inverted_index.manipulations | 30 |
| abstract_inverted_index.transformers, | 126 |
| abstract_inverted_index.classification | 132 |
| abstract_inverted_index.simultaneously | 57 |
| abstract_inverted_index.\textit{MoNFAP} | 192 |
| abstract_inverted_index.underdeveloped. | 32 |
| abstract_inverted_index.Mixture-of-Noises | 108 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile |