An Analysis on Image Forgery Detection Techniques

Main Article Content

Dayanand G. Savakar
Raju Hiremath
Anand Ghuli

Abstract

In the recent trends and technology we found that the digital image plays an important role. The digital images are widely used in various fields. The validation is now becomes a challenging task because of advancement in the tools and technology. The various tools are used to modify the digital image to create fake digital images. The several research has been carried out in the field of image forgery detection. In this paper attempted to review the existing technique to identify the forgery in an image. Accordingly related papers are reviewed and analyzed. The active and passive are the two approaches in the image forgery detection technique.  The digital signature and digital watermarking are the two methods in active approach, and in passive approach various techniques are there such as  copy-move, splicing, re-sampling etc., In this paper we describe the comparative analysis of different types of image forgery detection technique along with future work to be carried out.

Article Details

How to Cite
1.
Dayanand G. Savakar, Raju Hiremath, Anand Ghuli. An Analysis on Image Forgery Detection Techniques. J. Int. Acad. Phys. Sci. [Internet]. 2023 Jun. 15 [cited 2024 May 17];27(2):171-85. Available from: https://www.iaps.org.in/journal/index.php/journaliaps/article/view/973
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References

S. Mushtaq and A. H. Mir; Digital Image Forgeries and Passive Image Authentication Techniques: A Survey, International Journal of Advanced Science and Technology, 73, (2014), 15-32, doi: 10.14257/ijast.2014.73.02

K. N. Madhusudhan and P. Sakthivel; Combining Digital Signature with Local Binary Pattern-Least Significant Bit Steganography Techniques for Securing Medical Images, Journal of Medical Imaging and Health Informatics, 10(6), (2020), 1288-1293, doi: 10.1166/jmihi.2020.3015

S. Pramanik, S. K. Bandyopadhyay and R. Ghosh; Signature Image Hiding in Color Image using Steganography and Cryptography based on Digital Signature Concepts, Proceedings of the Second International Conference on Innovative Mechanisms for Industry Applications (ICIMIA 2020), (2020), 665-669.

O. Tayan, M. N. Kabir, and Y. M. Alginahi; A Hybrid Digital-Signature and Zero-Watermarking Approach for Authentication and Protection of Sensitive Electronic Documents, Scientific World Journal, 2014, (2014), doi: 10.1155/2014/514652.

M. Hussan, S. A. Parah, A. Jan, and G. J. Qureshi; A Hybrid Domain Fragile Watermarking Technique for Authentication of Color Images, Proceedings of International Conference on Computational Intelligence and Data Engineering, Lecture Notes on Data Engineering and Communications Technologies, (2022), 57–64. doi: 10.1007/978-981-16-7182-1_5.

R. Sinhal and I. A. Ansari; Multipurpose Image Watermarking: Ownership Check, Tamper Detection and Self-recovery, Circuits, Systems, and Signal Processing, (2022), doi: 10.1007/s00034-021-01926-z.

U. Khadim, M. Munwar Iqbal, and M. Awais Azam; A Secure Digital Text Watermarking Algorithm for Portable Document Format (PDF), Mehran University Research Journal of Engineering and Technology, 41(1), (2022), 100–110, doi: 10.22581/muet1982.2201.10.

M. Hussan, S. A. Parah, A. Jan, and G. J. Qureshi; Hash-based image watermarking technique for tamper detection and localization, Health and Technology, (2022), doi: 10.1007/s12553-021-00632-9.

P. V. Sanivarapu; Adaptive tamper detection watermarking scheme for medical images in transform domain, Multimedia Tools and Applications, (2022), doi: 10.1007/s11042-022-12273-9.

N. Kumar and T. Meenpal; Salient keypoint-based copy–move image forgery detection, Australian Journal of Forensic Sciences, (2022), 1-24.

G. Tahaoğlu, and G. Ulutas; Copy-move forgery detection and localization with hybrid neural network approach, Pamukkale Univ Muh Bilim Derg, (2022), 1-13.

T. Qazi, M. Ali, K. Hayat, and B. Magnier; Seamless Copy-Move Replication in Digital Images, Journal of Imaging, 8(69), (2022), 1-15.

Y. Gan, J. Zhong, and C. Vong; A Novel Copy-Move Forgery Detection Algorithm via Feature Label Matching and Hierarchical Segmentation Filtering, Information Processing and Management, 59(1), (2022), doi: 10.1016/j.ipm.2021.102783.

D. G. Savakar and Raju Hiremath; A Passive Image Forgery Detection Technique, Webology, 18(6), (2021), 768-778

A. Parnak, Y. B. Damavandi, S. J. Kazemitabar; A Novel Image Splicing Detection Algorithm Based on Generalized and Traditional Benford’s Law, International Journal of Engineering, 35(04), (2022), 626-634.

S. Agrawal, P. Kumar, S. Seth, T. Parag, M. Singh, and V. Babu; SISL:Self-Supervised Image Signature Learning for Splicing Detection and Localization, 2022, [Online]. Available: http://arxiv.org/abs/2203.07824

H. A. Jalab, M. A. Alqarni, R. W. Ibrahim, and A. Ali Almazroi; A novel pixel’s fractional mean-based image enhancement algorithm for better image splicing detection, Journal of King Saud University - Science, 34(2), (2022), doi: 10.1016/j.jksus.2021.101805.

X. Bi, Z. Zhang, Y. Liu, B. Xiao, and W. Li; Multi-Task Wavelet Corrected Network for Image Splicing Forgery Detection and Localization, 2021 IEEE International Conference on Multimedia and Expo (ICME), (2021), 1-6. doi: 10.1109/icme51207.2021.9428466.

T. Yang, J. Wu, and Z. Fang; Image Tampering Detection for Splicing based on Rich Feature and Convolution Neural Network, in ACM International Conference Proceeding Series, (2020), 82-86. doi: 10.1145/3409501.3409530.

S. M. Patil and M Malini; Extraction of image resampling using correlation aware convolution neural networks for image tampering detection, International Journal of Electrical & Computer Engineering (2088-8708), 12(3), (2022), 3033-3043.

R. Mehta, P. Kaushik, and N. Agarwal; Image Re-sampling Forgery Detection with Ensemble Classifier, International Journal of Advanced Science and Technology, 29(4), (2020), 4930-4943.

T. Qiao, A. Zhu, and F. Retraint; Exposing image resampling forgery by using linear parametric model, Multimedia Tools and Applications, 77(2), (2018), 1501-1523, , doi: 10.1007/s11042-016-4314-1.

A. Flenner, L. Peterson, J. Bunk, T. M. Mohammed, L. Nataraj, and B. S. Manjunath; Resampling forgery detection using deep learning and acontrario analysis, IS&T International Symposium on Electronic Imaging 2018 Media Watermarking, Security, and Forensics 2018, (2018). doi: 10.2352/ISSN.2470-1173.2018.07.MWSF-212.

B. Bayar and M. C. Stamm; On The Robustness Of Constrained Convolutional Neural Networks To Jpeg Post-Compression For Image Resampling Detection, ICASSP 2017, (2017), 2152-2156.

D. G. Savakar and R. Hiremath; Copy-move image forgery detection using shannon entropy, in Advances in Intelligent Systems and Computing. International Conference on Intelligent Computing and Control Systems (ICCS). IEEE, 1155, (2020), 76-90, doi: 10.1007/978-981-15-4029-5_8.2019.

D. Savakar, R. Hiremath, A. Ghuli; Copy-Move and Splicing Forgery Detection Using PNN. Design Engineering, (2021), 17151-17161.

Dayanand G. Savakar, Raju Hiremath and Anand Ghuli; Image Forgery Detection Using Hybrid Approach, Advanced Engineering Science, 54(2) , (2022), 1917-1931.

S. Bibi, A. Abbasi, I. U. Haq, S. W. Baik, and A. Ullah; Digital Image Forgery Detection Using Deep Autoencoder and CNN Features, Human-centric Computing and Information Sciences, 11, (2021), doi: 10.22967/HCIS.2021.11.032.

G. Mariappan, A.R. Satish, P.V. Bhaskar Reddy and B.Maram; Adaptive Partitioning Based Copy-Move Image Forgery Detection Using Optimal Enabled Deep Neuro-Fuzzy Network, Computational Intelligence, (2021), doi:101111/coin.12484.

I. T. Ahmed, B. T. Hammad, and N. Jamil; Image Copy-Move Forgery Detection Algorithms Based on Spatial Feature Domain, in Proceeding - 2021 IEEE 17th International Colloquium on Signal Processing and Its Applications, CSPA (2021), 92–96. doi: 10.1109/CSPA52141.2021.9377272.

M. N. Abbas, M. S. Ansari, M. N. Asghar, N. Kanwal, T. O’Neill, and B. Lee; Lightweight Deep Learning Model for Detection of Copy-Move Image Forgery with Post-Processed Attacks, in SAMI 2021 - IEEE 19th World Symposium on Applied Machine Intelligence and Informatics, Proceedings, (2021), 125-130. doi: 10.1109/SAMI50585.2021.9378690.

B. Rakesh Babu and S. N. Reddy; Copy-Move Forgery Detection in Digital Images Based on deep Learning, International Journal of Innovative Research in Science, Engineering and Technology, 10(2), (2021), doi: 10.15680/IJIRSET.2021.1002028.

S. Khan and A. Ali; CLIFD: A novel image forgery detection technique using digital signatures, Journal of Engineering Research (Kuwait), 9(1), (2021), 168-175, doi: 10.36909/JER.V9I1.8379.

L. Kaur, and S. S. Dhaliwal; Copy Move Forgery Detection In Digital Images Using Improved SIFT (I-SIFT) Approach, International Journal of Engineering Sciences & Research Technology, (2020), doi: 10.5281/zenodo.3700407.

S. Uma and P. D. Sathya; Copy-move forgery detection of digital images using football game optimization, Australian Journal of Forensic Sciences, (2020), doi: 10.1080/00450618.2020.1811376.

H. Chen, X. Yang, and Y. Lyu; Copy-move forgery detection based on keypoint clustering and similar neighborhood search algorithm, IEEE Access, 8, (2020), 36863-36875, doi: 10.1109/ACCESS.2020.2974804.

A. K. Jaiswal and R. Srivastava; A technique for image splicing detection using hybrid feature set, Multimedia Tools and Applications, 79, (17), (2020), 11837-11860, doi: 10.1007/s11042-019-08480-6.

N. A. Kurien, Danya S, D. Ninan, Heera Raju C and J. David; Accurate And Efficient Copy-Move Forgery Detection, 9th International Conference on Advances in Computing and Communication (ICACC), (2019), 130-135.

A. Kuznetsov; Digital image forgery detection using deep learning approach, in Journal of Physics: Conference Series, 1368(3), (2019), doi: 10.1088/1742-6596/1368/3/032028.

V. T. Manu and B. M. Mehtre; Copy-move tampering detection using affine transformation property preservation on clustered keypoints, Signal, Image and Video Processing, 12(3), (2018), 549–556, doi: 10.1007/s11760-017-1191-7.