An Improved CNN Steganalysis Architecture Based on “Catalyst Kernels” and Transfer Learning

Beji, Rabii El; Saidi, Marwa; Hermassi, Houcemeddine; Rhouma, Rhouma

doi:10.1007/978-3-319-97749-2_9

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…Concerning payload mismatch, a general idea [23,24] was to first train the model at a high payload and further fine-tune the model with new data at low payload asymptotically. Finally, a steganalysis model that can detect steganographic methods at a low embedding rate is obtained.…”

Section: Related Workmentioning

confidence: 99%

Dataset mismatched steganalysis using subdomain adaptation with guiding feature

Zhang

Abdullahi

et al. 2022

Telecommun Syst

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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.

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Section: Related Workmentioning

confidence: 99%

Dataset mismatched steganalysis using subdomain adaptation with guiding feature

Zhang

Abdullahi

et al. 2022

Telecommun Syst

View full text Add to dashboard Cite

show abstract

“…J-NET proposed in [22] alleviates this decline to a certain extent, and it also belongs to the construction of the domain adaptation classifier method. Concerning payload mismatch, a general idea [23,24] was to first train the model at a high payload and further finetune the model with new data at low payload asymptotically.…”

Section: Related Workmentioning

confidence: 99%

“…To date, many works [16][17][18][19][20][21][22][23][24] have attempted to solve the problem of CSM. We observed that few works are focusing on…”

Section: Introductionmentioning

confidence: 99%

Feature-guided Deep Subdomain Adaptation Network for Dataset Mismatch in Spatial Steganalysis

Zhang

Wang

et al. 2021

Preprint

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Steganalysis aims to detect covert communication established via steganography. In recent years, numerous deep learning-based image steganalysis methods with high performance have been proposed. However, these methods tend to suffer from distinct performance degradation when cover images in the train and test set are quite different, also known as cover source mismatch. To address this limitation, in this paper, a feature-guided deep subdomain adaptation network is proposed. Initially, the predictions of the pretrained model are used as pseudo labels to divide the unlabeled samples of the target domain into different subdomains, and the distributions of the relevant subdomains are aligned by subdomain adaptation. Afterwards, since the steganalysis model may assign incorrect predictions to samples in the target domain, we integrate guiding features to make the division of subdomains more precise. The experimental results show that the proposed network is significantly better than other three networks such as Steganalysis Residual Network (SRNet), deep adaptive network (J-Net) and Deep Subdomain Adaptation Network (DSAN), when it is used to detect three spatial steganographic algorithms with a wide variety of datasets and payloads. Especially, compared with SRNet, the average accuracy of our method is increased by 5.4% at 0.4bpp and 8.5% at 0.2bpp in the case of dataset mismatch.

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Analysis of Deep Learning-Based Image Steganalysis Methods Under Different Steganographic Algorithms

Dwaik¹,

Belkhouche²

2022

Advances in Visual Computing

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An Improved CNN Steganalysis Architecture Based on “Catalyst Kernels” and Transfer Learning

Cited by 5 publications

References 11 publications

Dataset mismatched steganalysis using subdomain adaptation with guiding feature

Dataset mismatched steganalysis using subdomain adaptation with guiding feature

Feature-guided Deep Subdomain Adaptation Network for Dataset Mismatch in Spatial Steganalysis

Analysis of Deep Learning-Based Image Steganalysis Methods Under Different Steganographic Algorithms

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