دورية أكاديمية
A robust self-supervised image hashing method for content identification with forensic detection of content-preserving manipulations.
| العنوان: | A robust self-supervised image hashing method for content identification with forensic detection of content-preserving manipulations. |
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| المؤلفون: | Fonseca-Bustos J; Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Luis Enrique Erro No. 1, Sta. Ma. Tonantzintla, 72840, Puebla, Mexico. Electronic address: jesus.fonseca@inaoep.mx., Ramírez-Gutiérrez KA; Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Luis Enrique Erro No. 1, Sta. Ma. Tonantzintla, 72840, Puebla, Mexico; Consejo Nacional de Humanidades, Ciencias y Tecnologías (CONAHCyT), Av. Insurgentes Sur 1582, Col. Crédito Constructor, Alcaldía Benito Juárez, C.P. 03940, Ciudad, Mexico. Electronic address: kramirez@inaoep.mx., Feregrino-Uribe C; Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Luis Enrique Erro No. 1, Sta. Ma. Tonantzintla, 72840, Puebla, Mexico. Electronic address: cferegrino@inaoep.mx. |
| المصدر: | Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2024 Sep; Vol. 177, pp. 106357. Date of Electronic Publication: 2024 May 03. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Pergamon Press Country of Publication: United States NLM ID: 8805018 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2782 (Electronic) Linking ISSN: 08936080 NLM ISO Abbreviation: Neural Netw Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: New York : Pergamon Press, [c1988- |
| مواضيع طبية MeSH: | Algorithms* , Image Processing, Computer-Assisted*/methods , Neural Networks, Computer*, Humans ; Deep Learning ; Supervised Machine Learning ; Forensic Sciences/methods |
| مستخلص: | Image content identification systems have many applications in industry and academia. In particular, a hash-based content identification system uses a robust image hashing function that computes a short binary identifier summarizing the perceptual content in a picture and is invariant against a set of expected manipulations while being capable of differentiating between different pictures. A common approach to designing these algorithms is crafting a processing pipeline by hand. Unfortunately, once the context changes, the researcher may need to define a new function to adapt. A deep hashing approach exploits the feature learning capabilities in deep networks to generate a feature vector that summarizes the perceptual content in the image, achieving outstanding performance for the image retrieval task, which requires measuring semantic and perceptual similarity between items. However, its application to robust content identification systems is an open area of opportunity. Also, image hashing functions are valuable tools for image authentication. However, to our knowledge, its application to content-preserving manipulation detection for image forensics tasks is still an open research area. In this work, we propose a deep hashing method exploiting the metric learning capabilities in contrastive self-supervised learning with a new modular loss function for robust image hashing. Moreover, we propose a novel approach for content-preserving manipulation detection for image forensics through a sensitivity component in our loss function. We validate our method through extensive experimentation in different data sets and configurations, validating the generalization properties in our work. Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (Copyright © 2024. Published by Elsevier Ltd.) |
| فهرسة مساهمة: | Keywords: Content identification; Image processing; Robust image hashing; Self-supervised learning |
| تواريخ الأحداث: | Date Created: 20240524 Date Completed: 20240615 Latest Revision: 20240615 |
| رمز التحديث: | 20240616 |
| DOI: | 10.1016/j.neunet.2024.106357 |
| PMID: | 38788289 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1879-2782 |
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| DOI: | 10.1016/j.neunet.2024.106357 |