A New Face Region Recovery Algorithm based on Bicubic Interpolation

Muntadher Al-Hadaad - Al-Iraqia University, Baghdad, 10001, Iraq
Rasha Thabit - Dijlah University College, Baghdad, 10001, Iraq
Khamis Zidan - Al-Iraqia University, Baghdad, 10001, Iraq

Citation Format:

DOI: http://dx.doi.org/10.30630/joiv.7.3.1671


Recently, researchers focused on face image manipulation detection and localization techniques because of their importance in image security applications. The previous research has not highlighted the recovery of the face region after manipulation detection. This paper presents a new face region recovery algorithm (FRRA) to be included in the face image manipulation detection algorithms (FIMD). The proposed FRRA consists of two main algorithms: face data generation algorithm and face region restoration algorithm. Both algorithms start by detecting the face region using Multi-task Cascaded Neural Network followed by a face window selection process. In the face data generation algorithm, the recovery information is generated from the shirked face window using bicubic interpolation technique. In the face region restoration algorithm, the face region zoomed using bicubic interpolation technique. The proposed FRRA has been tested and compared with previous recovery methods for different color face images, and the results proved that the FRRA could recover the face region with better visual quality at the same data length compared to previous methods. The main contributions of this research are a) the suggestion of including a face region recovery algorithm to FIMD, b) the study of previous recovery data generation algorithms for color face images, and c) introducing a new algorithm for generating the recovery data based on bicubic interpolation. In the future, the proposed algorithm can be included in the recent FIMD algorithms to recover the face region, which can be very useful in practical applications, especially those used in data forensics systems.


Face image security; face image manipulations detection; bicubic interpolation; image forensics

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M. Ibsen, C. Rathgeb, D. Fischer, P. Drozdowski, and C. Busch, “Digital Face Manipulation in Biometric Systems BT - Handbook of Digital Face Manipulation and Detection: From DeepFakes to Morphing Attacks,†C. Rathgeb, R. Tolosana, R. Vera-Rodriguez, and C. Busch, Eds. Cham: Springer International Publishing, 2022, pp. 27–43. doi: 10.1007/978-3-030-87664-7_2.

J. Cote, “DEEPFAKES AND FAKE NEWS POSE A GROWING THREAT TO DEMOCRACY, EXPERTS WARN,†News Northeast., 2022, [Online]. Available: https://news.northeastern.edu/2022/04/01/deepfakes-fake-news-threat-democracy/

L. Patel, “The Rise of Deepfakes and What That Means for Identity Fraud,†DarkReading Authentication, 2020, [Online]. Available: https://www.darkreading.com/authentication/the-rise-of-deepfakes-and-what-that-means-for-identity-fraud

D. Citron, “How DeepFake undermine truth and threaten democracy,†2019. [Online]. Available: https://www.ted.com/talks/danielle_citron_how_deepfakes_undermine_truth_and_threaten_democracy

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.

B. Zi, M. Chang, J. Chen, X. Ma, and Y.-G. Jiang, “WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection,†in Proceedings of the 28th ACM International Conference on Multimedia, New York, NY, USA: Association for Computing Machinery, 2020, pp. 2382–2390.

C. Gray, “Add to Cart: Why deepfakes are good for retail,†AdNews Newsl., 2020.

S. Kolagati, T. Priyadharshini, and V. Mary Anita Rajam, “Exposing deepfakes using a deep multilayer perceptron – convolutional neural network model,†Int. J. Inf. Manag. Data Insights, vol. 2, no. 1, p. 100054, 2022, doi: https://doi.org/10.1016/j.jjimei.2021.100054.

C. Rathgeb, R. Tolosana, R. Vera-Rodriguez, and C. Busch, Handbook of Digital Face Manipulation and Detection (From DeepFakes to Morphing Attacks). Springer Cham, 2022. doi: 10.1007/978-3-030-87664-7.

A. Kohli and A. Gupta, “Detecting DeepFake, FaceSwap and Face2Face facial forgeries using frequency CNN,†Multimed. Tools Appl., vol. 80, no. 12, pp. 18461–18478, 2021, doi: 10.1007/s11042-020-10420-8.

D. Siegel, C. Kraetzer, S. Seidlitz, and J. Dittmann, “Media Forensics Considerations on DeepFake Detection with Hand-Crafted Features,†J. Imaging, vol. 7, no. 7, p. 108, Jul. 2021, doi: 10.3390/jimaging7070108.

I. Papastratis, “Deepfakes: Face synthesis with GANs and Autoencoders,†AI Summer, 2020, [Online]. Available: https://theaisummer.com/deepfakes/

P. Korus, “Digital image integrity – a survey of protection and verification techniques,†Digit. Signal Process., vol. 71, pp. 1–26, 2017, doi: https://doi.org/10.1016/j.dsp.2017.08.009.

I. Amerini, G. Baldini, and F. Leotta, “Image and Video Forensics.,†Journal of imaging, vol. 7, no. 11. Nov. 2021. doi: 10.3390/jimaging7110242.

J. Harish Kumar and T. Kirthiga Devi, “Fingerprinting of Image Files Based on Metadata and Statistical Analysis BT - Proceedings of International Conference on Deep Learning, Computing and Intelligence,†2022, pp. 105–118.

A. Berthet and J.-L. Dugelay, “A review of data preprocessing modules in digital image forensics methods using deep learning,†in 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP), 2020, pp. 281–284. doi: 10.1109/VCIP49819.2020.9301880.

D. Cozzolino, A. Rössler, J. Thies, M. Nießner, and L. Verdoliva, “ID-Reveal: Identity-aware DeepFake Video Detection,†in 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 15088–15097. doi: 10.1109/ICCV48922.2021.01483.

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.

L. A. Passos, D. Jodas, K. A. P. da Costa, L. A. S. Júnior, D. Colombo, and J. P. Papa, “A Review of Deep Learning-based Approaches for Deepfake Content Detection,†arXiv:2202.06095v1, 2022, [Online]. Available: http://arxiv.org/abs/2202.06095

A. M. Almars, “Deepfakes Detection Techniques Using Deep Learning: A Survey,†J. Comput. Commun., vol. 09, no. 05, pp. 20–35, 2021, doi: 10.4236/jcc.2021.95003.

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.

B. Dolhansky, R. Howes, B. Pflaum, N. Baram, and C. C. Ferrer, “The deepfake detection challenge (dfdc) preview dataset,†arXiv Prepr. arXiv1910.08854, 2019.

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.

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), 2022, no. 1001, pp. 1–6. doi: 10.1109/iicaiet55139.2022.9936838.

R. Thabit and B. E. Khoo, “A new robust lossless data hiding scheme and its application to color medical images,†Digit. Signal Process. A Rev. J., vol. 38, 2015, doi: 10.1016/j.dsp.2014.12.005.

R. Thabit and B. E. Khoo, “A new robust reversible watermarking method in the transform domain,†Lect. Notes Electr. Eng., vol. 291 LNEE, 2014, doi: 10.1007/978-981-4585-42-2_19.

R. Thabit and B. E. Khoo, “Robust reversible watermarking scheme using Slantlet transform matrix,†J. Syst. Softw., vol. 88, no. 1, 2014, doi: 10.1016/j.jss.2013.09.033.

J. M. Zain and A. R. M. Fauzi, “Medical Image Watermarking with Tamper Detection and Recovery,†2006. doi: https://doi.org/10.1109/IEMBS.2006.260767.

K. H. Chiang, K. C. Chang-Chien, R. F. Chang, and H. Y. Yen, “Tamper detection and restoring system for medical images using wavelet-based reversible data embedding,†J. Digit. Imaging, vol. 21, no. 1, pp. 77–90, 2008, doi: 10.1007/s10278-007-9012-0.

M. M. B. Kulkarni and R. T. Patil, “Tamper Detection & Recovery in Medical Image with secure data hiding using Reversible watermarking,†Int. J. Emerg. Technol. Adv. Eng., vol. 2, no. 3, pp. 1–4, 2012.

J. M. Zain and A. R. M. Fauzi, “Evaluation of medical image watermarking with tamper detection and recovery (AW-TDR).,†Conf. Proc. IEEE Eng. Med. Biol. Soc., pp. 5662–5665, 2007.

R. Thabit and B. E. Khoo, “Medical image authentication using SLT and IWT schemes,†Multimed. Tools Appl., vol. 76, no. 1, pp. 309–332, 2017, doi: 10.1007/s11042-015-3055-x.

S. Fadnavis, “Image Interpolation Techniques in Digital Image Processing: An Overview,†Journal of Engineering Research and Applications www.ijera.com, vol. 4, no. 10. pp. 70–73, 2014. [Online]. Available: www.ijera.com

R. Walia, “Zooming Digital Images using Modal,†Interpolat. Int. J. Appl. or Innov. Eng. Manag., vol. 2, no. 5, 2013, [Online]. Available: https://www.researchgate.net/publication/274702043