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- Title
Dataset mismatched steganalysis using subdomain adaptation with guiding feature.
- Authors
Zhang, Lei; Abdullahi, Sani M.; He, Peisong; Wang, Hongxia
- Abstract
The generalization problem in deep learning has always been an important problem to be solved. In the field of steganalysis, generalization is also an important factor that makes steganalysis models difficult to deploy in real-world scenarios. For a group of suspicious images that never appeared in the training set, the pre-trained deep learning-based steganalysis models tend to suffer from distinct performance degradation. To address this limitation, in this paper, a feature-guided subdomain adaptation steganalysis framework is proposed to improve the performance of the pre-trained models when detecting new data. Initially, the source domain and target domain will be divided into subdomains according to class, and the distributions of the relevant subdomains are aligned by subdomain adaptation. Afterward, the guiding feature is generated to make the division of subdomains more stable and precise. When it is used to detect three spatial steganographic algorithms with a wide variety of datasets and payloads, the experimental results show that the proposed steganalysis framework can significantly improve the average accuracy of SRNet model by 5.4% at 0.4bpp, 8.5% at 0.2bpp, and 8.0% at 0.1bpp in the case of dataset mismatch.
- Subjects
DEEP learning; PROBLEM solving; PHYSIOLOGICAL adaptation; GENERALIZATION
- Publication
Telecommunication Systems, 2022, Vol 80, Issue 2, p263
- ISSN
1018-4864
- Publication type
Article
- DOI
10.1007/s11235-022-00901-6