Recent Apporaches for Facemask Forgery Detection: Review

Authors

  • Shivani Chaudhry Research Scholar, Department of Computer Science and Engineering, Starex University, Binola (Gurugaon) Haryana,122413
  • Krishna Kumar Singh Associate professor, Symbiosis Centre for Information Technology, Pune

Keywords:

Facemasks Detection, Object Detection, Algorithms, Technologies, Forgery

Abstract

Facemasks are a challenging task to detect. It has attracted increasing attention in this era. Consequently, there is an essential and challenging difficulty in identifying face masks as it is easier for a mask to recognise the face, but mask recognition is critical since removing the mask face is very complex. A multi-stage procedure is used in traditional object detection. Over the last two decades, there has been an increase in the interest in virtual face stimulation. Today, advanced technology acts as a forgery for processing digital photographs and computer graphics. Using digital images to falsify is one of the most significant technological issues. However, law enforcement specialists are developing robust algorithms to eliminate counterfeiting systems. With this field contributing huge impetus, the latest advances in profound learning enable several new applications like the CNT to assist and utilise computer vision technology (CNNs). Face recognition has been one of the most significant research subjects in computer vision and biometrics in the recent decade. All Algorithms generally depend on elements, such as low resolution, illumination, expressions, which deteriorate their precision.

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References

Tahaoğlu, G.; Ulutas, G.; Gençtürk, B. A New Robust Copy Move Forgery Detection Method.; October 5 2020; pp. 1–4.

Kumar, V.; kansal, V.; Gaur, M. A Comprehensive Analysis on Video Forgery Detection Techniques. SSRN Electron. J. 2020, doi:10.2139/ssrn.3563382.

Benhamza, H.; Djeffal, A.; Cheddad, A. A Review of Image Forgery Detection.; October 7 2020.

Khudhair, Z.; Mohamed, F.; Abdulameer, K. A Review on Copy-Move Image Forgery Detection Techniques. J. Phys. Conf. Ser. 2021, 1892, 012010, doi:10.1088/1742-6596/1892/1/012010.

Rana, S. A Study on Image Forgery Detection. Int. J. Res. Appl. Sci. Eng. Technol. 2020, 8, 364–369, doi:10.22214/ijraset.2020.31882.

Zhang, Y.; Gao, F.; Zhou, Z.; Guo, H. A Survey on Face Forgery Detection of Deepfake.; June 30 2021; p. 46.

Gurunlu, B.; Ozturk, S. A Survey on Photo Forgery Detection Methods. ITM Web Conf. 2018, 22, 01055, doi:10.1051/itmconf/20182201055.

KN, S.; Chennamma, H. A Survey On Video Forgery Detection. 2015.

Kumar, D.; Karthi, S.; Karthika, K.; R., C. A Systematic Study of Image Forgery Detection. J. Comput. Theor. Nanosci. 2018, 15, 2560–2564.

Kumar, D.; R., C. A Systematic Study of Image Forgery Detection; 2018; Vol. 15;.

Kurien, N.; Danya, S.; Ninan, D.; Raju, C.; David, J. Accurate And Efficient Copy-Move Forgery Detection.; November 1 2019; pp. 130–135.

Tassia, R.; Kurdy, M.-B. Adaptive and Improved Approach for Image Forgery Detection. 2020.

Kaur, C.; Kanwal, N. An Analysis of Image Forgery Detection Techniques. Stat. Optim. Inf. Comput. 2019, 7, doi:10.19139/soic.v7i2.542.

Bhole, P. An Image Forgery Detection Using SIFT-PCA. Int. J. Eng. Res. 2020, V9, doi:10.17577/IJERTV9IS060127.

Lin, Y.-K.; Chang, C.-Y. Face Forgery Detection Based on Deep Learning. In; 2020; pp. 270–278 ISBN 978-3-030-46827-9.

Sethi, S.; Kathuria, M.; Kaushik, T. Face Mask Detection Using Deep Learning: An Approach to Reduce Risk of Coronavirus Spread. J. Biomed. Inform. 2021, 120, 103848, doi:10.1016/j.jbi.2021.103848.

Issa, H.; Saleh, I.; Hadri, S.; Abdulrahman, Y. Forgery Detection and Localisation in Scanned Documents.; December 28 2020.

Khan, S.; Khan, K.; Ali, F.; Kwak, K. Forgery Detection and Localisation of Modifications at the Pixel Level. Symmetry 2020, 12, doi:10.3390/sym12010137.

Mahfoudi, G.; Morain-Nicolier, F.; Restraint, F.; Pic, M. Object-Removal Forgery Detection Through Reflectance Analysis.; December 9 2020.

Luo, Y.; Zhang, Y.; Yan, J.; Liu, W. Generalizing Face Forgery Detection with High-Frequency Features; 2021;

Hyun, D.-K.; Lee, M.-J.; Ryu, S.-J.; Lee, H.-Y.; Lee, H.-K. Forgery Detection for Surveillance Video. Era Interact. Media 2013, 25–36, doi:10.1007/978-1-4614-3501-3_3.

Sapra, P.; Kikan, V. Analysis of Various Copy-Move Forgery Detection Techniques. In; 2021; pp. 400–405 ISBN 978-1-00-319383-8.

Ratta, P.; Kaur, A.; Sharma, S.; Shabaz, M.; Dhiman, G. Application of Blockchain and Internet of Things in Healthcare and Medical Sector: Applications, Challenges, and Future Perspectives. J. Food Qual. 2021, 2021, e7608296, doi:10.1155/2021/7608296.

Chandani, K.; Arora, M. Automatic Facial Forgery Detection Using Deep Neural Networks. In; 2021; pp. 205–214 ISBN 9789811599552.

Hebbar, N.; Kunte, A. Blind Forgery Detection in Digital Images: An Analysis. In; 2020; pp. 604–614 ISBN 978-3-030-30464-5.

Nazli, M.; Maghari, A. Comparison between Image Forgery Detection Algorithms.; May 1 2017; pp. 442–445.

Rani, H.; Sharma, N. Copy Move Forgery Detection with Hybrid Approach. 2020, 9, 2585.

Roy, A.; Dixit, R.; Naskar, R.; Chakraborty, R. Copy-Move Forgery Detection in Transform Domain. In Studies in Computational Intelligence; 2020; pp. 79–86 ISBN 978-981-10-7643-5.

Kharanghar, M.; Doegar, A. Copy-Move Forgery Detection Methods: A Critique. In; 2021; pp. 501–523 ISBN 9789811554209.

Mandankandy, A.; Shanmugam, P. Dbelm for Image Forgery Detection. 2020, 7, 2348–6090.

Khan, J. Copy-Move Forgery Detection Using Deep Learning; 2021;

Dogonadze, N.; Obernosterer, J.; Hou, J. Deep Face Forgery Detection; 2020;

Shah, Y.; Shah, P.; Patel, M.; Khamkar, C.; Kanani, P. Deep Learning Model-Based Multimedia Forgery Detection. 2020, doi:10.1109/I-SMAC49090.2020.9243530.

Wang, Y.; Hu, Y.; Liew, A.W.-C.; Li, C.-T. ENF Based Video Forgery Detection Algorithm. Int. J. Digit. Crime Forensics 2020, 12, 131–156, doi:10.4018/IJDCF.2020010107.

Dang, T.; Beghdadi, A.; Larabi, C. Blind Inpainting Forgery Detection.; December 5 2014.

Katherineguo, J.; Daviddoermann; Azrielrosenfeld FORGERY DETECTION BY LOCAL CORRESPONDENCE. Int. J. Pattern Recognit. Artif. Intell. 2011, 15, doi:10.1142/S0218001401001088.

Lyu, Q.; Luo, J.; Liu, K.; Yin, X.; Liu, J.; Wei, Z. Copy Move Forgery Detection Based on Double Matching. J. Vis. Commun. Image Represent. 2021, 76, 103057, doi:10.1016/j.jvcir.2021.103057.

Sharma, H.; Kanwal, N. Video Interframe Forgery Detection: Classification, Technique

Aljaberi, A. Topological Data Analysis for Image Forgery Detection. Indian J. Forensic Med. Toxicol. 2020, 14, 1745–1751, doi:10.37506/ijfmt.v14i3.

Menezes, H.; Ferreira, A.; Pereira, E.; Gomes, H. Bias and Fairness in Face Detection.; October 21 2021.

Zecheng, T.; Xinyuan, W.; Hongli, Y.; Yansong, Z. U 2 -Net for Image Forgery Detection and Localisation.; April 1 2021; pp. 166–172.

Keimel, C. Video Quality. In; 2016; pp. 11–29 ISBN 978-981-10-0268-7.

Kong, R.; Zhang, B. An Effective New Algorithm for Face Recognition.; January 1 2015.

Jhansi, G.; Rama, G.; Ranganath, K.; Juluri, T.; Vinay, C.; Reddy, K.; Chalamalla, V. Face Detection Authentication Analysis on Smartphones Face Detection Authentication Analysis on Smartphones. Mater. Sci. Eng. 2021, 981, 2026.

Mufti, T. FACE DETECTION AND RECOGNITION; 2020;

Tathe, V.; Narote, A.; Narote, S. Face Detection and Recognition in Videos.; December 1 2016; pp. 1–6.

Parekh, D. Face Detection and Recognition; 2020;

TARIQ, I.; Mufti, T. FACE DETECTION AND RECOGNITION; 2020;

Nobahar Sadeghi Nam, A. Face Detection. Int. J. Innov. Sci. Res. Technol. 2020, 5, 688–692, doi:10.38124/IJISRT20SEP391.

Loy, C.C. Face Detection. In; 2021; pp. 429–434 ISBN 978-3-030-63415-5.

Raut, J.; Patil, S.; Gawade, S.; Meena, M. Face Detection and Recognition in Video.; March 15 2015.

Daniya, T.; Gladiss, N.; R., C. Study on Digital Image Forgery Detection. 2020, 29, 6851–6856.

Khan, J. Random Forest-Based Copy-Move Forgery Detection; 2020;

Jin, X.; He, Z.; Xu, J.; Wang, Y.; Su, Y. Object-Based Video Forgery Detection via Dual-Stream Networks.; July 5 2021; pp. 1–6.

Uma, R.; Sathya, A. Soccer Game Optimization Based Forgery Detection of Digital Images. Forensic Imaging 2021, 25, 200453, doi:10.1016/j.fri.2021.200453.

Pavlovic, A.; Milosavljević, N.; Gavrovska, A.; Reljin, I. Copy-Move Forgery Detection Based on Multifractals. Multimed. Tools Appl. 2019, 78, doi:10.1007/s11042-019-7277-1.

Mandankandy, A.; Shanmugam, P. Random Region Rotation Based Image Forgery Detection 2021.

Daniya, T.; Thirukrishna, J.T.; Kumar, D.; Kumar, M. ICSA-ECNN Based Image Forgery Detection in Face Images.; April 22 2021.

M, M.; Khurshid, F. Digital Image Forgery Detection Approaches: A Review. In; 2021; pp. 863–882 ISBN 978-981-334-603-1.

Kumar, V.; Kansal, V.; Gaur, M. A Comprehensive Survey on Passive Video Forgery Detection Techniques. In; 2021; pp. 39–57 ISBN 9789811584688.

Kaur, S.; Kaur, M. Novel Method for Copy-Move Forgery Detection. Int. J. Comput. Appl. 2021, 174, 10–14, doi:10.5120/ijca2021921064.

AlZahir, S.; Hammad, R. Image Forgery Detection Using Image Similarity. Multimed. Tools Appl. 2020, 79, doi:10.1007/s11042-020-09502-4.

Kumar, B.; Kumar, K. Blockchain Solution for Evidence Forgery Detection. J. Comput. Theor. Nanosci. 2020, 17, 5570–5576, doi:10.1166/jctn.2020.9454.

V, B.; P, A.; A, K.; N, G.; S, C. Forgery Detection in Documents. Int. J. Electron. Commun. Eng. 2018, 5, 15–20, doi:10.14445/23488549/IJECE-V5I5P104.

Peplow, M. Holograms Aid Forgery Detection. Nature 2004, doi:10.1038/news040809-1.

Diwan, A.; Sharma, R.; Roy, A.; Mitra, S. Keypoint Based Comprehensive Copy‐move Forgery Detection. IET Image Process. 2020, 15, doi:10.1049/ipr2.12105.

Alenezi, A.; Sapar, S.; Rakhimov, I. FORGERY DETECTION USING KRAWTCHOUK MOMENTS. Far East J. Math. Sci. 2019, 111, 181–194, doi:10.17654/MS111020181.

Cao, S.; Zou, Q.; Mao, X.; Wang, Z. Metric Learning for Anti-Compression Facial Forgery Detection; 2021;

Shah, S.; Raskar, P. METHODS FOR FORGERY DETECTION IN DIGITAL FORENSICS. Int. J. Electron. Secur. Digit. Forensics 2021, 1, 1, doi:10.1504/IJESDF.2021.10035056.

Privman-Horesh, N.; Haider, A.; Hel-Or, H. Forgery Detection in 3D-Sensor Images.; June 1 2018; pp. 1642–16428.

Satpute, R.; Shubham, P.; Mansi, P.; Shah, S.; Aditi, S. Image Forgery Detection & Localization. SSRN Electron. J. 2021, doi:10.2139/ssrn.3838598.

Wang, Xiang yang; Wang, C.; Wang, L.; Yang, H.; Niu, P. Robust and Effective Multiple Copy-Move Forgeries Detection and Localisation. Pattern Anal. Appl. 2021, doi:10.1007/s10044-021-00968-y.

Ojeda-Aciego, M.; Rodríguez-Jiménez, J. Formal Concept Analysis with Negative Attributes for Forgery Detection. Comput. Math. Methods 2020, doi:10.1002/cmm4.1124.

Chen, S.; Yao, T.; Chen, Y.; Ding, S.; Li, J.; Ji, R. Local Relation Learning for Face Forgery Detection; 2021;

Jaffar, R.; Rasool, Z.; Hanon AlAsadi, A. New Copy-Move Forgery Detection Algorithm.; September 1 2019; pp. 1–5.

Z. Wang, G. Wang, B. Huang, Z. Xiong, Q. Hong, H. Wu, P. Yi, K. Jiang, N. Wang, Y. Pei, H. Chen, Y. Miao, Z. Huang, and J. Liang, ‘‘Masked face recognition dataset and application,’’ 2020.

Real World Masked Face Recognition Dataset. Accessed: Jun. 17, 2021.

S. Ge, J. Li, Q. Ye, and Z. Luo, ‘‘Detecting masked faces in the wild with LLE-CNNs,’’ in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jul. 2017, pp. 2682–2690

A. Cabani, K. Hammoudi, H. Benhabiles, and M. Melkemi, ‘‘MaskedFace Net—A dataset of correctly/incorrectly masked face images in the context of COVID-19,’’ Smart Health, vol. 19, Mar. 2021, Art. no. 1001.

G. B. Huang, M. Mattar, T. Berg, and E. Learned-Miller, ‘‘Labeled faces in the wild: A database forstudying face recognition in unconstrained environments,’’ in Proc. Workshop Faces ‘Real-Life’ Images, Detection, Alignment, Recognit. Marseille, France: Erik Learned-Miller and Andras Ferencz and Frédéric Jurie, Oct. 2008, pp. 1–15.

Larxel’s Face Mask Detection Dataset. Accessed: Jun. 17, 2021.

W. Bu, J. Xiao, C. Zhou, M. Yang, and C. Peng, ‘‘A cascade framework for masked face detection,’’ in Proc. IEEE Int. Conf. Cybern. Intell. Syst. (CIS), IEEE Conf. Robot., Autom. Mechatronics (RAM), Nov. 2017, pp. 458–462

B. Qin and D. Li, ‘‘Identifying facemask-wearing condition using image super-resolution with classification network to prevent COVID-19,’’ Sen sors, vol. 20, no. 18, p. 5236, Sep. 2020.

M. Inamdar and N. Mehendale, ‘‘Real-time face mask identification using facemasknet deep learning network,’’ India, Jul. 2020.

A. Chavda, J. Dsouza, S. Badgujar, and A. Damani, ‘‘Multi-stage CNN architecture for face mask detection,’’ 2020, arXiv:2009.07627.

S. Yadav, ‘‘Deep learning based safe social distancing and face mask detection in public areas for COVID-19 safety guidelines adherence,’’ Int. J. Res. Appl. Sci. Eng. Technol., vol. 8, no. 7, pp. 1368–1375, Jul. 2020.

M. Jiang, X. Fan, and H. Yan, ‘‘RetinaMask: A face mask detector,’’ 2020, arXiv:2005.03950.

S. V. Militante and N. V. Dionisio, ‘‘Real-time facemask recognition with alarm system using deep learning,’’ in Proc. 11th IEEE Control Syst. Graduate Res. Colloq. (ICSGRC), Aug. 2020, pp. 106–110

P. Khandelwal, A. Khandelwal, S. Agarwal, D. Thomas, N. Xavier, and A. Raghuraman, ‘‘Using computer vision to enhance safety of workforce in manufacturing in a post COVID world,’’ 2020, arXiv:2005.05287.

M. T. C. Jagadeeswari, ‘‘Performance evaluation of intelligent face mask detection system with various deep learning classifiers,’’ Int. J. Adv. Sci. Technol., vol. 29, no. 11, pp. 3083–3087, May 2020.

M. Loey, G. Manogaran, M. H. N. Taha, and N. E. M. Khalifa, ‘‘Fighting against COVID-19: A novel deep learning model based on YOLO-v2 with ResNet-50 for medical face mask detection,’’ Sustain. Cities Soc., vol. 65, Feb. 2021, Art. no. 102600.

N.-C. Ristea and R. T. Ionescu, ‘‘Are you wearing a mask? Improv ing mask detection from speech using augmentation by cycle-consistent GANs,’’ 2020, arXiv:2006.10147

A. Nieto-Rodríguez, M. Mucientes, and V. M. Brea, ‘‘System for med ical mask detection in the operating room through facial attributes,’’ in Proc. Iberian Conf. Pattern Recognit. Image Anal. Spain: Springer, 2015, pp. 138–145.

M. Loey, G. Manogaran, M. H. N. Taha, and N. E. M. Khalifa, ‘‘A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic,’’ Measurement, vol. 167, Jan. 2021, Art. no. 108288

V. V. V. Vinitha, ‘‘COVID-19 facemask detection with deep learning and computer vision,’’ Int. Res. J. Eng. Technol., vol. 7, no. 8, pp. 3127–3132, 2020

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Published

24.03.2024

How to Cite

Chaudhry, S. ., & Singh, K. K. . (2024). Recent Apporaches for Facemask Forgery Detection: Review. International Journal of Intelligent Systems and Applications in Engineering, 12(18s), 781–799. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5167

Issue

Vol. 12 No. 18s (2024)

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Research Article

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