JPEG steganalysis with combined dense connected CNNs and SCA-GFR

The detection of weakly hidden information in a JPEG compressed image is challenging. In this paper, we propose a 32-layer convolutional neural network (CNN) involving feature reuse by concatenating all features from previous layers. The proposed method can improve the flow of gradient, and the shar...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Multimedia tools and applications Ročník 78; číslo 7; s. 8481 - 8495
Hlavní autoři: Yang, Jianhua, Kang, Xiangui, Wong, Edward K., Shi, Yun-Qing
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.04.2019
Springer Nature B.V
Témata:
Accuracy
Artificial neural networks
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Error detection
Feature maps
Filtration
Image compression
Image detection
JPEG encoders-decoders
Methods
Model accuracy
Multimedia
Multimedia Information Systems
Neural networks
Noise
Special Purpose and Application-Based Systems
Adaptive steganography
Convolutional neural networks
JPEG image steganalysis
Ensemble
ISSN:1380-7501, 1573-7721
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:The detection of weakly hidden information in a JPEG compressed image is challenging. In this paper, we propose a 32-layer convolutional neural network (CNN) involving feature reuse by concatenating all features from previous layers. The proposed method can improve the flow of gradient, and the sharing of features and bottleneck layers can also dramatically reduce the number of parameters in the proposed CNN model. To further improve the detection accuracy and combine the directional features from the selection-channel-aware Gabor filtering residual (SCA-GFR) method with Gabor filtering and non-directional feature maps from the CNN model, an ensemble architecture called CNN-SCA-GFR is used, which combines the proposed CNN method with the conventional SCA-GFR method to detect J-UNIWARD and UERD. This can significantly reduce the detection error rate to below that of the existing JPEG steganalysis methods. For example, in the detection of J-UNIWARD at 0.1 bpnzAC, the detection error rate using our proposed method is 5.67% lower than that achieved by XuNet, and 7.89% lower than that achieved by the conventional SCA-GFR method. When detecting UERD at 0.1 bpnzAC, the detection error rate using our proposed method is 5.94% lower than that achieved by XuNet, and 10.28% lower than that achieved by the conventional SCA-GFR method.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-018-6878-4