Academic Journal
Side-Informed Steganography for JPEG Images by Modeling Decompressed Images
| العنوان: | Side-Informed Steganography for JPEG Images by Modeling Decompressed Images |
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| المؤلفون: | Butora, Jan, Bas, Patrick |
| المساهمون: | Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS), European Project: 101021687,H2020,H2020-SU-SEC-2018-2019-2020,UNCOVER(2021) |
| المصدر: | ISSN: 1556-6013 ; IEEE Transactions on Information Forensics and Security ; https://hal.science/hal-04083163 ; IEEE Transactions on Information Forensics and Security, In press, ⟨10.1109/TIFS.2023.3268884⟩. |
| بيانات النشر: | HAL CCSD Institute of Electrical and Electronics Engineers |
| سنة النشر: | 2023 |
| المجموعة: | LillOA (HAL Lille Open Archive, Université de Lille) |
| مصطلحات موضوعية: | Steganography, side information, JPEG, decompressed image, [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] |
| الوصف: | International audience ; Side-informed steganography has always been among the most secure approaches in the field. However, a majority of existing methods for JPEG images use the side information, here the rounding error, in a heuristic way. For the first time, we show that the usefulness of the rounding error comes from its covariance with the embedding changes. Unfortunately, this covariance between continuous and discrete variables is not analytically available. An estimate of the covariance is proposed, which allows to model steganography as a change in the variance of DCT coefficients. Since steganalysis today is best performed in the spatial domain, we derive a likelihood ratio test to preserve a model of a decompressed JPEG image. The proposed method then bounds the power of this test by minimizing the Kullback-Leibler divergence between the cover and stego distributions. We experimentally demonstrate in two popular datasets that it achieves state-of-the-art performance against deep learning detectors. Moreover, by considering a different pixel variance estimator for images compressed with Quality Factor 100, even greater improvements are obtained. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| Relation: | info:eu-repo/grantAgreement//101021687/EU/Development of an efficient steganalysis framework for uncovering hidden data in digital media/UNCOVER; hal-04083163; https://hal.science/hal-04083163; https://hal.science/hal-04083163/document; https://hal.science/hal-04083163/file/SI_LRT_07.pdf |
| DOI: | 10.1109/TIFS.2023.3268884 |
| الاتاحة: | https://hal.science/hal-04083163 https://hal.science/hal-04083163/document https://hal.science/hal-04083163/file/SI_LRT_07.pdf https://doi.org/10.1109/TIFS.2023.3268884 |
| Rights: | http://hal.archives-ouvertes.fr/licences/copyright/ ; info:eu-repo/semantics/OpenAccess |
| رقم الانضمام: | edsbas.8DE44648 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1109/TIFS.2023.3268884 |
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