AuCFSR: Authentication and Color Face Self-Recovery Using Novel 2D Hyperchaotic System and Deep Learning Models
Abstract
:1. Introduction
- Introducing a new 2D hyperchaotic system called 2D-MSCM.
- Analyzing the chaotic behavior of 2D-MSCM and demonstrating its superiority over similar maps.
- Introducing of a new approach for color face image authentication and self-recovery (AuCFSR) based on 2D-MSCM chaotic system and deep learning models.
- AuCFSR incorporates the watermark and recovery data into 2-LSB of the cover image, which ensures high visual quality of the AuCFSR output image.
- The suggested 2D-MSCM ensures the high security level of the proposed AuCFSR, as its security keys are very sensitive to any variation by +/−10−15.
- AuCFSR is a pixel-based system, which guarantees a high precision in tampering detection process.
- The use of deep learning models in the post-processing leads to improving the visual quality of the recovered color face image.
- To the best of our knowledge, AuCFSR is the first authentication and self-recovery scheme designed for color face images.
2. Related Work
- The proposed scheme is pixel-based, which can provide high accuracy in detecting the tampered areas. Therefore, an improvement in the tampering detection accuracy is expected by using the proposed scheme.
- The proposed method integrates the watermark and recovery data into 2 LSBs of each pixel. Therefore, our method ensures low degradation of the host image.
- Our scheme uses the pseudorandom property of the proposed 2D-MSCM to construct the watermark data and to embed the latter with the recovery data into the input image. In addition, the chaotic property of 2D-MSCM is exploited to overcome the problem of tampering coincidence. To the best of our knowledge, this is the first exploitation of chaotic systems in overcoming this problem.
- The proposed scheme involves a post-processing stage that relies on pre-trained deep learning models for improving the recovered image quality. Therefore, an improvement in the quality of the recovered image is expected via our scheme over the latest state-of-the-art schemes.
- The performance of our system is illustrated by its application to the tampering detection and self-recovery of color face images. To the best of our knowledge, this is the first attempt to address such specific problem in the image authentication and self-recovery application.
- The robustness of the provided scheme against brute force attacks and the sensitivity of the security keys are investigated to prove its high level of security.
3. Novel 2-D Discrete Hyperchaotic Map and Its Analysis
3.1. Lyapunov Exponents
3.2. Bifurcation Behaviour
3.3. Phase Attractors
4. Proposed Scheme for Color Face Image Authentication and Self-Recovery
4.1. 2D-MSCM-Based Color Image Fragile Watermarking
Algorithm 1. LSB-based color face image watermarking. | |
Inputs | W: Binary watermark of size generated by the proposed 2D chaotic map I: Input color image of size |
Output | WI: The watermarked color image |
//Splitting the input I image into three color image channels | |
1. | Get the red channel (R) of the input image (I) |
2. | Get the green channel (G) of the input image (I) |
3. | Get the blue channel (B) of the input image (I) |
4. | Set the first LSBs of B to W bits, which generates B* channel |
//Generating the watermarked color image (WI) | |
5. | |
6. | |
7. | |
8. | Return WI |
4.2. Self-Recovery Data Embedding
Algorithm 2. Proposed 2D-MSCM based confusion Algorithm. | |
Inputs: | Xc: Confusion vector of size generated based on 2D-MSCM Yc: Confusion vector of size generated based on 2D-MSCM I: 2D matrix of size |
Output: | CI: The confused version of I matrix |
1. | for i = 1 to N do |
2. | Get value, which is the i-element in Xc vector |
3. | = CircShift (, )//where CircShift (, ) is a left circular shifting function operation, which shifts the elements of i-row in I matrix by positions. |
4. | end for |
5. | for j = 1 to M do |
6. | Get value, which is the j-element in Yc vector |
7. | = CircShift (, )//where CircShift (, ) is a left circular shifting operation, which shifts the elements of j-columnin in matrix by positions. |
8. | end for |
9. | for i = 1 to N do |
10. | Get value, which is the i-element in Xc vector |
11. | = CircShift (, ) |
12. | end for |
13. | Return CI |
Algorithm 3. Proposed Algorithm for the recovery data embedding into the LSBs of the watermarked image channels. | |
Inputs: | Rb*, Gb*, and Bb*: three binary matrices of size , and , respectively WI: Watermarked color image of size |
Output: | WI*: Watermarked color image with embedded self-recovery data |
//Splitting WI image into three color image channels | |
1. | Get the red channel (RW) of WI image |
2. | Get the green channel (GW) of WI image |
3. | Get the blue channel (BW) of WI image |
4. | Get the first layer, noted R1, of the Rb* matrix |
5. | Get the second layer, noted R2, of the Rb* matrix |
6. | Get the first layer, noted G1, of the Gb* matrix |
7. | Get the second layer, noted G2, of the Gb* matrix |
8. | Set the 1-LSBs of the RW pixels to R1 bits |
9. | Set the 2-LSBs of the RW pixels to R2 bits, which generates RW* channel |
10. | Set the 1-LSBs of the GW pixels to G1 bits |
11. | Set the 2-LSBs of the GW pixels to G2 bits, which generates GW* channel |
12. | Set the 2-LSBs of the BW pixels to Bb* bits, which generates BW* channel |
//Generating the watermarked color image with the self-recovery data (WI*) | |
13. | |
14. | |
15. | |
16. | Return WI* |
4.3. Blind Detection of the Tampered Areas
Algorithm 4. Proposed algorithm for localizing the tampered areas within the received image. | |
Inputs: | W: Binary watermark of size generated via the proposed 2D-MSCM. RI: Received color image of size |
Outputs: | TZ: Binary matrix that represents the tampered regions (pixel positions) within the RI image TZ_NOT: The logical NOT of TZ matrix |
//Splitting WI image into three color image channels | |
1. | Get the red channel (R) of RI image |
2. | Get the green channel (G) of RI image |
3. | Get the blue channel (B) of RI image |
4. | Get the first LSB values of B channel and then save these values in WI* matrix. |
5. | T = XOR(WI*,W)//where the symbol “XOR” denotes the Bit-wise XOR operation between two binary inputs |
6. | TZ = IMCLOSE(T,3)//where IMCLOSE(TZ,3) function [41] performs the morphological closing with radius of 3 pixels on T binary image. |
7. | TZ_NOT = NOT(TZ)//where NOT (TZ) operator performs the logical NOT of TZ logical input |
8. | Return TZ, TZ_NOT |
4.4. Cropping the Detected Tampred Zones Withing The Received Image
4.5. Self-Recovery Image Generation
Algorithm 5. Proposed algorithm for generating the self-recovery image. | |
Inputs: | RI_Crop: Received image of size with cropped tampered zones TZ: The logical matrix of the tampered zones Xc: Confusion vector of size Yc: Confusion vector of size |
Output: | SRI: Generated self-recovery image of size |
1. | Get the red channel (R) of RI_Crop image |
2. | Get the green channel (G) of RI_Crop image |
3. | Get the blue channel (B) o of RI_Crop image |
4. | Get the first LSB values of R channel and then save these values in R_c1 matrix |
5. | Get the second LSB values of R channel and then save these values in R_c2 matrix |
6. | Get the first LSB values of G channel and then save these values in G_c1 matrix |
7. | Get the second LSB values of G channel and then save these values in G_c2 matrix |
8. | Get the second LSB values of B channel and then save these values in B_c2 matrix. |
9. | Perform the inverse confusion process for R_c1 matrix using Inv_confusion function described in Algorithm 6 to get R1 matrix |
10. | Perform the inverse confusion process for R_c2 matrix using Inv_confusion function to get R2 matrix |
11. | Perform the inverse confusion process for G_c1 matrix using Inv_confusion function to get G1 matrix |
12. | Perform the inverse confusion process for G_c2 matrix using Inv_confusion function to get G2 matrix |
13. | Perform the inverse confusion process for B_c2 matrix using Inv_confusion function to get B2 matrix |
//The following steps are implemented for generating the self-recovery image | |
14. | SRI_R1 = R1.*TZ//where the symbol “.*” represents the Hadamard product operation |
15. | SRI_R2 = R2.*TZ |
16. | SRI_G1 = G1.*TZ |
17. | SRI_G2 = G2.*TZ |
18. | SRI_B2 = G2.*TZ |
19. | Create an matrix, noted SRI, of zero values represented on 8 bits |
20. | Set the 7-MSBs of the first SRI layer to SRI_R1 binary matrix |
21. | Set the 8-MSBs of the first SRI layer to SRI_R2 binary matrix |
22. | Set the 7-MSBs of the second SRI layer to SRI_G1 binary matrix |
23. | Set the 8-MSBs of the second SRI layer to SRI_G2 binary matrix |
24. | Set the 8-MSBs of the third SRI layer to SRI_B2 binary matrix |
25. | Return SRI |
Algorithm 6. Proposed inverse confusion algorithm (Inv_Confusion) based on 2D-MSCM. | |
Inputs: | Xc: Confusion vector of size generated based on 2D-MSCM Yc: Confusion vector of size generated based on 2D-MSCM CI:Confused 2D matrix of size |
Output: | I: The inverse confused version of CI matrix |
1. | for i = 1 to N do |
2. | Get value, which is the i-element in Xc vector |
3. | = CircShift (, ) |
4. | end for |
5. | for j = 1 to M do |
6. | Get value, which is the j-element in Yc vector |
7. | = CircShift (,) |
8. | end for |
9. | for i = 1 to N do |
10. | Get value, which is the i-element in Xc vector |
11. | = CircShift (,) |
12. | end for |
13. | Return I |
4.6. Deep Learning-Based Post-Processing of the Recovered Image
5. Simulation Results
5.1. Evaluation of Imperceptibility Performance
5.2. Evaluation of Tampering Detection Rate Performance
5.3. Evaluation of Image Recovery Performance
5.4. Evaluation of Security Performance
5.4.1. Key Space Analysis
5.4.2. Key Sensitivity Analysis
5.5. Comparison with Similar Work
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Scheme’s Reference | Spatial/ Transform Domain | Block/Pixel-Based | Embedding Data Position (8 Bit Deep) | Image Category | Addressing the Tamper Coincidence Problem | Analysis of the Security Level? | Use of Deep Learning Techniques? |
---|---|---|---|---|---|---|---|
Aminuddin et al. [3] | Spatial | Block-based | 2-LSB | Color | Yes | No | No |
Aminuddin et al. [4] | Spatial | Block-based | 2-LSB | Color | Yes | No | No |
Molina-Garcia et al. [8] | Spatial | Block-based | 2-LSB | Color | Yes | No | No |
Tong et al. [18] | Spatial | Block-based | 3-LSB | Color | No | No | No |
Singh et al. [19] | DCT Transform | Block-based | 3-LSB | Grayscale/Color | Yes | No | No |
Cao et al. [20] | Spatial | Block-based | 2-LSB | Grayscale | Yes | No | No |
Tai et al. [21] | DWT Transform | Block-based | 2-LSB | Grayscale | Yes | Yes | No |
Fan et al. [22] | Spatial | Block-based | 2-LSB | Grayscale | No | No | No |
Bolourian Haghighi et al. [23] | LWT transform | Block-based | 2-LSB | Grayscale/Color | No | No | No |
Li et al. [24] | Spatial | Block-based | 2-LSB | Grayscale | No | No | No |
Al-Otum et al. [25] | Hybrid (spatial and DWT transform) | Block-based | 2-LSB | Color | Yes | No | No |
Su et al. [26] | Spatial | Block-based | 2-LSB | Medical Grayscale | Yes | No | No |
Rezaei et al. [27] | DCT Transform | Block-based | 2-LSB | Grayscale | No | No | Yes |
Proposed | Spatial | Pixel-based | 2-LSB | Color Face image | Yes | Yes | Yes |
Attacks | Proportion of Attacked Image Area | ||||
---|---|---|---|---|---|
5% | 10% | 15% | 20% | 25% | |
Cropping | 42.0127 | 39.3090 | 36.3278 | 32.1698 | 28.1234 |
Copy-move | 43.1398 | 40.811 | 35.7809 | 33.0643 | 27.1678 |
Face swapping | 42.5678 | 38.9865 | 35.1245 | 31.2236 | 28.5567 |
Object addition | 43.4567 | 38.1289 | 35.4887 | 32.0097 | 26.1309 |
Scheme | Proportions of Copied-Moved Areas | |||||
---|---|---|---|---|---|---|
10% | 20% | 40% | 60% | 80% | ||
Schemes | Proposed | 0.9988 | 0.9983 | 0.9978 | 0.9977 | 0.9976 |
Aminuddin et al. [3] | 0.9852 | 0.9806 | 0.9736 | 0.9719 | 0.9702 | |
Aminuddin et al. [4] | 0.9852 | 0.9806 | 0.9736 | 0.9719 | 0.9702 | |
Molina-Garcia et al. [8] | 0.9516 | 0.9632 | 0.9502 | 0.9703 | 0.9695 | |
Al-Otum et al. [25] | 0.9420 | 0.9302 | 0.9455 | 0.9560 | 0.9400 |
Attacks | Schemes | ||||
---|---|---|---|---|---|
Proposed | Aminuddin et al. [3] | Aminuddin et al. [4] | Molina-Garcia et al. [8] | Al-Otum et al. [25] | |
Cropping (rate of 25%) | 28.3616 | 24.9063 | 25.1633 | 23.1613 | 21.3685 |
Cropping (rate of 50%) | 23.0625 | 21.1696 | 21.635 | 20.1632 | 18.7452 |
Copy-move (rate of 25%) | 27.3611 | 24.1696 | 25.1698 | 23.6354 | 22.1696 |
Copy-move (rate of 50%) | 23.5063 | 21.1596 | 21.0056 | 20.1785 | 19.6321 |
Face swapping (rate 25%) | 26.8852 | 24.0258 | 24.9820 | 22.9621 | 20.1633 |
Face swapping (rate 50%) | 22.9630 | 20.9523 | 20.9816 | 20.1632 | 18.6592 |
Object addition (20% rate) | 32.1622 | 29.1652 | 30.1487 | 27.1632 | 26.1233 |
Object addition (40% rate) | 29.6305 | 27.0029 | 27.1598 | 25.1436 | 24.1678 |
Scheme Features | Schemes | ||||
---|---|---|---|---|---|
Proposed | Aminuddin et al. [3] | Aminuddin et al. [4] | Molina-Garcia et al. [8] | Al-Otum et al. [25] | |
Watermarking method (block-based/Pixel-based) | Pixel-based | Block-based | Block-based | Block-based | Block-based |
Key space | - | - | - | - | |
Use of deep learning models? | Yes | No | No | No | No |
Data embedding domain | Spatial | Spatial | Spatial | Spatial | Hybrid |
Salt-and-pepper noise robustness? | Yes | - | - | - | Yes |
Gaussian noise robustness? | No | - | - | - | No |
Average runtime for watermarking and recovery data embedding | 1.0512 | 3.2346 | 2.1754 | 4.6580 | 5.9561 |
Average runtime for tamper detection and data recovery | 15.7416 | 9.1644 | 10.3498 | 10.1230 | 12.1678 |
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Daoui, A.; Yamni, M.; Altameem, T.; Ahmad, M.; Hammad, M.; Pławiak, P.; Tadeusiewicz, R.; A. Abd El-Latif, A. AuCFSR: Authentication and Color Face Self-Recovery Using Novel 2D Hyperchaotic System and Deep Learning Models. Sensors 2023, 23, 8957. https://doi.org/10.3390/s23218957
Daoui A, Yamni M, Altameem T, Ahmad M, Hammad M, Pławiak P, Tadeusiewicz R, A. Abd El-Latif A. AuCFSR: Authentication and Color Face Self-Recovery Using Novel 2D Hyperchaotic System and Deep Learning Models. Sensors. 2023; 23(21):8957. https://doi.org/10.3390/s23218957
Chicago/Turabian StyleDaoui, Achraf, Mohamed Yamni, Torki Altameem, Musheer Ahmad, Mohamed Hammad, Paweł Pławiak, Ryszard Tadeusiewicz, and Ahmed A. Abd El-Latif. 2023. "AuCFSR: Authentication and Color Face Self-Recovery Using Novel 2D Hyperchaotic System and Deep Learning Models" Sensors 23, no. 21: 8957. https://doi.org/10.3390/s23218957
APA StyleDaoui, A., Yamni, M., Altameem, T., Ahmad, M., Hammad, M., Pławiak, P., Tadeusiewicz, R., & A. Abd El-Latif, A. (2023). AuCFSR: Authentication and Color Face Self-Recovery Using Novel 2D Hyperchaotic System and Deep Learning Models. Sensors, 23(21), 8957. https://doi.org/10.3390/s23218957