Steganalysis of Context-Aware Image Steganography Techniques Using Convolutional Neural Network
Abstract
:1. Introduction
- The proposed technique highlights the high-frequency elements using a high-boost filter in the first non-trainable convolutional layer. It improves the detection accuracy by more than one percent.
- Thirty high-pass filtered images were generated using SRM filters in the second non-trainable convolutional layer to give prominence to the noise of the stego-image effectively.
- A single pooling layer in the last part of the CNN was used to sustain the complete statistical traces from each layer.
- A clipped ReLU layer was introduced for customized thresholding to obtain more statistical information.
- The SVM classifier was utilized instead of the softmax classifier to increase the detection performance. The SVM classifier outperforms in many applications.
- Experimental results of the proposed technique were compared with SRNet, Ye-Net, Yedroudj-Net, and Zhu-Net. The experimental results are displayed for the HILL, S-UNIWARD, and WOW steganography algorithms with payloads of 0.2, 0.3, and 0.4 bits per pixel.
- In the detailed experimental analysis, the proposed technique was proven to be better than the existing techniques with a higher detection accuracy.
2. The Proposed Scheme
3. Experimental Analysis
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Payload (bpp) | HILL | S-UNIWARD | WOW |
---|---|---|---|
0.2 | 45.43 | 40.58 | 53.33 |
0.3 | 46.95 | 39.87 | 53.79 |
0.4 | 48.61 | 41.27 | 54.49 |
S. No. | Steganography Technique/ Payload (bpp) | HILL | S-UNIWARD | WOW | ||||||
---|---|---|---|---|---|---|---|---|---|---|
0.2 | 0.3 | 0.4 | 0.2 | 0.3 | 0.4 | 0.2 | 0.3 | 0.4 | ||
1 | SRM + ReLU | 60.33 | 67.09 | 69.69 | 68.05 | 76.27 | 82.35 | 73.46 | 80.48 | 84.75 |
2 | SRM + CReLU | 61.24 | 67.90 | 70.82 | 69.02 | 77.04 | 81.61 | 74.13 | 81.79 | 85.43 |
3 | HB + SRM + ReLU | 61.93 | 69.14 | 71.68 | 69.51 | 77.97 | 82.85 | 75.03 | 82.45 | 86.38 |
4 | HB + SRM + CReLU | 62.49 | 68.59 | 72.26 | 70.00 | 78.68 | 84.11 | 76.02 | 83.36 | 87.74 |
S. No. | Steganography Technique/ Payload (bpp) | HILL | S-UNIWARD | WOW | ||||||
---|---|---|---|---|---|---|---|---|---|---|
0.2 | 0.3 | 0.4 | 0.2 | 0.3 | 0.4 | 0.2 | 0.3 | 0.4 | ||
1 | Softmax Classifier | 62.49 | 68.59 | 72.26 | 70.00 | 78.68 | 84.11 | 76.02 | 83.36 | 87.74 |
2 | SVM classifier | 63.25 | 69.91 | 73.07 | 69.54 | 79.88 | 84.79 | 77.02 | 83.95 | 87.26 |
Steganography Technique/ Payload (bpp) | HILL | S-UNIWARD | WOW | ||||||
---|---|---|---|---|---|---|---|---|---|
0.2 | 0.3 | 0.4 | 0.2 | 0.3 | 0.4 | 0.2 | 0.3 | 0.4 | |
SRNet | 55.51 | 63.64 | 67.62 | 64.97 | 73.88 | 79.34 | 72.52 | 79.41 | 83.59 |
Ye-Net | 54.31 | 59.40 | 63.85 | 60.20 | 68.01 | 74.83 | 69.39 | 72.97 | 78.65 |
Yedroudj-Net | 54.09 | 58.72 | 67.98 | 59.74 | 69.29 | 74.97 | 69.96 | 75.52 | 81.07 |
Zhu-Net | 61.74 | 66.61 | 74.56 | 70.23 | 78.12 | 82.81 | 74.37 | 78.41 | 86.23 |
Proposed Method | 63.25 | 69.91 | 73.07 | 69.54 | 79.88 | 84.79 | 77.02 | 83.95 | 87.26 |
Steganography Technique/ Payload (bpp) | HILL | S-UNIWARD | WOW | ||||||
---|---|---|---|---|---|---|---|---|---|
0.2 | 0.3 | 0.4 | 0.2 | 0.3 | 0.4 | 0.2 | 0.3 | 0.4 | |
SRNet | 60.85 | 67.10 | 68.79 | 67.17 | 76.18 | 79.06 | 77.40 | 83.97 | 88.49 |
Ye-Net | 53.79 | 61.60 | 65.61 | 62.32 | 70.60 | 74.98 | 70.42 | 78.81 | 82.37 |
Yedroudj-Net | 57.28 | 64.14 | 66.91 | 63.51 | 71.28 | 74.15 | 73.49 | 81.93 | 85.46 |
Zhu-Net | 63.81 | 69.85 | 77.27 | 73.40 | 79.89 | 83.40 | 79.51 | 87.55 | 92.66 |
Proposed Method | 64.49 | 74.10 | 77.13 | 75.06 | 83.64 | 86.85 | 81.50 | 88.81 | 91.78 |
Steganography Technique/ Payload (bpp) | HILL | S-UNIWARD | WOW | ||||||
---|---|---|---|---|---|---|---|---|---|
0.2 | 0.3 | 0.4 | 0.2 | 0.3 | 0.4 | 0.2 | 0.3 | 0.4 | |
SRNet | 64.44 | 70.03 | 72.92 | 74.31 | 81.29 | 84.66 | 78.28 | 84.37 | 87.97 |
Ye-Net | 59.62 | 64.84 | 69.81 | 65.72 | 74.08 | 82.25 | 72.46 | 77.50 | 83.66 |
Yedroudj-Net | 60.94 | 66.93 | 70.17 | 67.03 | 75.40 | 80.02 | 74.25 | 82.66 | 86.85 |
Zhu-Net | 66.82 | 72.45 | 78.97 | 80.57 | 85.28 | 88.45 | 81.86 | 90.37 | 92.31 |
Proposed Method | 72.24 | 76.92 | 82.28 | 82.16 | 87.23 | 91.37 | 83.32 | 89.27 | 93.16 |
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Agarwal, S.; Kim, C.; Jung, K.-H. Steganalysis of Context-Aware Image Steganography Techniques Using Convolutional Neural Network. Appl. Sci. 2022, 12, 10793. https://doi.org/10.3390/app122110793
Agarwal S, Kim C, Jung K-H. Steganalysis of Context-Aware Image Steganography Techniques Using Convolutional Neural Network. Applied Sciences. 2022; 12(21):10793. https://doi.org/10.3390/app122110793
Chicago/Turabian StyleAgarwal, Saurabh, Cheonshik Kim, and Ki-Hyun Jung. 2022. "Steganalysis of Context-Aware Image Steganography Techniques Using Convolutional Neural Network" Applied Sciences 12, no. 21: 10793. https://doi.org/10.3390/app122110793
APA StyleAgarwal, S., Kim, C., & Jung, K.-H. (2022). Steganalysis of Context-Aware Image Steganography Techniques Using Convolutional Neural Network. Applied Sciences, 12(21), 10793. https://doi.org/10.3390/app122110793