Improved Face Image Authentication Scheme based on Embedding in Adjacent Coefficients

Asmaa Jawad - Al-Iraqia University, Baghdad, 10011, Iraq
Rasha Thabit - Al-Iraqia University, Baghdad, 10011, Iraq
Khamis A. Zidan - Al-Iraqia University, Baghdad, 10011, Iraq


Citation Format:



DOI: http://dx.doi.org/10.62527/joiv.8.3.2488

Abstract


Face image authentication (FIA) schemes have recently been developed using face detection and image watermarking technology. The research in this direction proved the presented schemes' efficiency in accurately detecting the manipulated face regions and recovering the original face region. Recovering the original face region is very important in practical applications. Still, it was at the cost of increasing the secret data that must be embedded in the face image. The increment in the secret data required a large embedding capacity, which was not available in some images. To overcome this limitation, an improved FIA scheme based on a new data embedding algorithm is presented in this paper. The suggested FIA scheme consists of two main algorithms applied at the sender and receiver sides, where both start by detecting the face region and dividing and classifying the image into blocks that belong to the face region or outside the face region. At the sender side, the secret data are generated from the face region and embedded in the blocks outside the face region using the suggested algorithm called Embedding in Adjacent Coefficients (EAC) for three subbands obtained after applying the Slantlet transform of the blocks. On the receiver side, the secret data are extracted from the blocks outside the face region using the suggested algorithm called Extraction from Adjacent Coefficients (ExAC). The extracted data is used to authenticate the face region and recover the original one when manipulations occur. The proposed FIA scheme obtained higher embedding capacity than previous ones, making it applicable to protect more face images that could not be protected using previous FIA schemes.

Keywords


Face Image Authentication; DeepFakes Reveal; Multimedia Forensics

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References


W. Zhang and C. Zhao, “Exposing Face-Swap Images Based on Deep Learning and ELA Detection,” in The 5th International Electronic Conference on Entropy and Its Applications, Basel Switzerland: MDPI, Nov. 2019, p. 29. doi: 10.3390/ecea-5-06684.

P. Majumdar, A. Agarwal, M. Vatsa, and R. Singh, “Facial Retouching and Alteration Detection,” C. Rathgeb, R. Tolosana, R. Vera-Rodriguez, and C. Busch, Eds., Cham: Springer International Publishing, 2022, pp. 367–387. doi: 10.1007/978-3-030-87664-7_17.

V. V. Vamsi et al., “Deepfake Detection in Digital Media Forensics,” Glob. Transitions Proc., 2022, doi: https://doi.org/10.1016/j.gltp.2022.04.017.

R. Thabit, “Review of Cryptography Applications in eHealth Security Systems,” Int. J. Sci. Eng. Investig., vol. 8, no. 89, pp. 110–116, 2019, [Online]. Available: http://www.ijsei.com/papers/ijsei-88919-16.pdf

A. Czajka, W. Kasprzak, and A. Wilkowski, “Verification of iris image authenticity using fragile watermarking,” Bull. Polish Acad. Sci. Tech. Sci., vol. 64, no. 4, pp. 807–819, 2016, doi: 10.1515/bpasts-2016-0090.

I. J. Goodfellow et al., “Generative adversarial nets,” in Proceedings of advances in neural information processing systems, 2014.

D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” in Proceedings of international conference on learning representations, 2013.

F. Matern, C. Riess, and M. Stamminger, “Exploiting Visual Artifacts to Expose Deepfakes and Face Manipulations,” in 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW), IEEE, Jan. 2019, pp. 83–92. doi: 10.1109/WACVW.2019.00020.

H. Li, B. Li, S. Tan, and J. Huang, “Identification of deep network generated images using disparities in color components,” Signal Processing, vol. 174, p. 107616, 2020, doi: https://doi.org/10.1016/j.sigpro.2020.107616.

U. Scherhag, D. Budhrani, M. Gomez-Barrero, and C. Busch, “Detecting Morphed Face Images Using Facial Landmarks BT - Image and Signal Processing,” A. Mansouri, A. El Moataz, F. Nouboud, and D. Mammass, Eds., Cham: Springer International Publishing, 2018, pp. 444–452.

Z. A. Salih, R. Thabit, K. A. Zidan, and B. E. Khoo, “Challenges of Face Image Authentication and Suggested Solutions,” in 2022 International Conference on Information Technology Systems and Innovation (ICITSI), 2022, pp. 189–193. doi: 10.1109/ICITSI56531.2022.9970797.

R. Tolosana, R. Vera-Rodriguez, J. Fierrez, A. Morales, and J. Ortega-Garcia, “Deepfakes and beyond: A Survey of face manipulation and fake detection,” Inf. Fusion, vol. 64, pp. 131–148, Dec. 2020, doi: 10.1016/j.inffus.2020.06.014.

S. Venkatesh, R. Ramachandra, K. Raja, and C. Busch, “Face Morphing Attack Generation and Detection: A Comprehensive Survey,” IEEE Trans. Technol. Soc., vol. 2, no. 3, pp. 128–145, 2021, doi: 10.1109/TTS.2021.3066254.

H. Kaur and S. R, “VLSI Implementation of Lightweight Cryptography Algorithm,” Adv. Syst. Sci. Appl., vol. 16, no. 1, pp. 95–101, 2016.

R. Thabit and B. E. Khoo, “Robust Reversible Watermarking Application for Fingerprint Image Security,” Adv. Syst. Sci. Appl., vol. 22, no. 1, pp. 117–129, 2022.

R. Thabit, “Improved Steganography Techniques For Different Types Of Secret Data,” Adv. Syst. Sci. Appl., vol. 19, no. 3, pp. 38–51, 2019.

S. H. Silva, M. Bethany, A. M. Votto, I. H. Scarff, N. Beebe, and P. Najafirad, “Deepfake forensics analysis: An explainable hierarchical ensemble of weakly supervised models,” Forensic Sci. Int. Synerg., vol. 4, p. 100217, 2022, doi: https://doi.org/10.1016/j.fsisyn.2022.100217.

D. K. Citron, How deepfakes undermine truth and threaten democracy. TED, 2019.

X. Ju, “An Overview of Face Manipulation Detection,” J. Cyber Secur., vol. 2, no. 4, pp. 197–207, 2020, doi: 10.32604/jcs.2020.014310.

M. Dang and T. N. Nguyen, “Digital Face Manipulation Creation and Detection: A Systematic Review,” Electronics, vol. 12, no. 16, p. 3407, Aug. 2023, doi: 10.3390/electronics12163407.

P. Yu, Z. Xia, J. Fei, and Y. Lu, “A Survey on Deepfake Video Detection,” IET Biometrics, vol. 10, no. 6, pp. 607–624, Nov. 2021, doi: 10.1049/bme2.12031.

M. Abdolahnejad and P. X. Liu, “Deep learning for face image synthesis and semantic manipulations: a review and future perspectives,” Artif. Intell. Rev., vol. 53, no. 8, pp. 5847–5880, Dec. 2020, doi: 10.1007/s10462-020-09835-4.

F. Juefei-Xu, R. Wang, Y. Huang, Q. Guo, L. Ma, and Y. Liu, “Countering malicious deepfakes: Survey, battleground, and horizon,” Int. J. Comput. Vis., vol. 130, no. 7, pp. 1678–1734, 2022.

T. T. Nguyen et al., “Deep learning for deepfakes creation and detection: A survey,” Comput. Vis. Image Underst., vol. 223, 2022, doi: 10.1016/j.cviu.2022.103525.

A. D. and S. B. Wankhade, Intelligent Computing and Networking, vol. 146. 2021.

E.-V. Pikoulis, Z.-M. Ioannou, M. Paschou, and E. Sakkopoulos, “Face Morphing, a Modern Threat to Border Security: Recent Advances and Open Challenges,” Appl. Sci., vol. 11, no. 7, 2021, doi: 10.3390/app11073207.

A. Rössler, D. Cozzolino, L. Verdoliva, C. Riess, J. Thies, and M. Niessner, “FaceForensics++: Learning to Detect Manipulated Facial Images,” in 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 1–11. doi: 10.1109/ICCV.2019.00009.

A. Malik, M. Kuribayashi, S. M. Abdullahi, and A. N. Khan, “DeepFake Detection for Human Face Images and Videos: A Survey,” IEEE Access, vol. 10, pp. 18757–18775, 2022, doi: 10.1109/ACCESS.2022.3151186.

B. Dolhansky et al., “The deepfake detection challenge (dfdc) dataset,” arXiv Prepr. arXiv2006.07397, 2020.

Z. A. Salih, R. Thabit, K. A. Zidan, and B. E. Khoo, “A new face image manipulation reveal scheme based on face detection and image watermarking,” in 2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), Bandung: IEEE, 2022, pp. 1–6. doi: 10.1109/iicaiet55139.2022.9936838.

Z. A. Salih, R. Thabit, and K. A. Zidan, “A New Manipulation Detection and Localization Scheme,” J. Eng. Sci. Technol., vol. 18, no. 2, pp. 1164–1183, 2023.

M. H. Al-Hadaad, R. Thabit, and K. A. Zidan, “A New Face Region Recovery Algorithm based on Bicubic Interpolation,” Int. J. Informatics Vis., vol. 7, no. 3, pp. 1000–1006, 2023, doi: 10.30630/joiv.7.3.1671.

M. H. Al-Hadaad, R. Thabit, and K. A. Zidan, “A New Face Image Authentication Scheme based on Bicubic Interpolation,” Al-Iraqia J. Sci. Eng. Res., vol. 2, no. 2, pp. 29–36, Jun. 2023, doi: 10.58564/IJSER.2.2.2023.68.

R. Thabit and B. E. Khoo, “A New Robust Reversible Watermarking Method in the Transform Domain BT - The 8th International Conference on Robotic, Vision, Signal Processing & Power Applications,” H. A. Mat Sakim and M. T. Mustaffa, Eds., Singapore: Springer Singapore, 2014, pp. 161–168.