A Noise and Edge extraction-based dual-branch method for Shallowfake and Deepfake Localization Article Swipe
Deepak Dagar
,
Dinesh Kumar Vishwakarma
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.00896
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.00896
The trustworthiness of multimedia is being increasingly evaluated by advanced Image Manipulation Localization (IML) techniques, resulting in the emergence of the IML field. An effective manipulation model necessitates the extraction of non-semantic differential features between manipulated and legitimate sections to utilize artifacts. This requires direct comparisons between the two regions.. Current models employ either feature approaches based on handcrafted features, convolutional neural networks (CNNs), or a hybrid approach that combines both. Handcrafted feature approaches presuppose tampering in advance, hence restricting their effectiveness in handling various tampering procedures, but CNNs capture semantic information, which is insufficient for addressing manipulation artifacts. In order to address these constraints, we have developed a dual-branch model that integrates manually designed feature noise with conventional CNN features. This model employs a dual-branch strategy, where one branch integrates noise characteristics and the other branch integrates RGB features using the hierarchical ConvNext Module. In addition, the model utilizes edge supervision loss to acquire boundary manipulation information, resulting in accurate localization at the edges. Furthermore, this architecture utilizes a feature augmentation module to optimize and refine the presentation of attributes. The shallowfakes dataset (CASIA, COVERAGE, COLUMBIA, NIST16) and deepfake dataset Faceforensics++ (FF++) underwent thorough testing to demonstrate their outstanding ability to extract features and their superior performance compared to other baseline models. The AUC score achieved an astounding 99%. The model is superior in comparison and easily outperforms the existing state-of-the-art (SoTA) models.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2409.00896
- https://arxiv.org/pdf/2409.00896
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402954213
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4402954213Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2409.00896Digital Object Identifier
- Title
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A Noise and Edge extraction-based dual-branch method for Shallowfake and Deepfake LocalizationWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
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2024-09-02Full publication date if available
- Authors
-
Deepak Dagar, Dinesh Kumar VishwakarmaList of authors in order
- Landing page
-
https://arxiv.org/abs/2409.00896Publisher landing page
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-
https://arxiv.org/pdf/2409.00896Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2409.00896Direct OA link when available
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Dual (grammatical number), Enhanced Data Rates for GSM Evolution, Extraction (chemistry), Noise (video), Computer science, Chromatography, Chemistry, Artificial intelligence, Art, Literature, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.shallowfakes | 182 |
| abstract_inverted_index.convolutional | 60 |
| abstract_inverted_index.effectiveness | 81 |
| abstract_inverted_index.Faceforensics++ | 191 |
| abstract_inverted_index.characteristics | 132 |
| abstract_inverted_index.trustworthiness | 1 |
| abstract_inverted_index.state-of-the-art | 231 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile |