OVERVIEW OF DIGITAL IMAGE FORGERY DETECTION

Petar Čisar

DOI Number
https://doi.org/10.2298/FUEE2504581C
First page
581
Last page
603

Abstract


Digital image forgery detection is a vital area of research in digital forensics, aiming to authenticate visual content in the face of increasingly sophisticated manipulation techniques. This paper presents a comprehensive overview of the field, integrating key concepts in its technical landscape. The categorization of detection methods typically includes active approaches that depend on embedded watermarks or signatures, and passive (blind) techniques that analyze the image content itself without prior information. Underlying these are various detection principles, such as identifying inconsistencies in pixel patterns, compression artifacts, illumination, and sensor noise.

The paper explores the specific characteristics of detection techniques, analyzing their strengths and limitations. Pixel-based and statistical methods offer efficiency for copy–move and splicing detection but often lack robustness under compression or scaling. Frequency-domain methods and physics-based analysis provide deeper insights, but they can be computationally intensive or sensitive to environmental conditions. The evaluation of detection models is crucial, relying on diverse datasets, realistic manipulation scenarios, and adversarial robustness testing. Effective evaluation metrics include accuracy, precision, recall, F1-score, AUC-ROC and IoU, which collectively assess classification and localization performance.

The deep learning approach in forgery detection has significantly advanced the field, with convolutional neural networks and transformer-based models learning complex tampering artifacts. However, challenges persist in forgery detection, including evolving manipulation methods, dataset limitations, explainability concerns, and vulnerability to adversarial attacks. Finally, the authors discuss trends and future directions, such as self-supervised learning, multimodal forensic integration, domain adaptation, and real-time detection frameworks, paving the way for more resilient and scalable forensic tools.


Keywords

image forgery detection, categorization, detection principles, characteristics, evaluation.

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References


P. Čisar, S. Maravić Čisar and S. Bošnjak, Cybercrime and digital forensics – technologies and approaches, DAAAM International Scientific Book, Vienna, Austria, 2014, Chapter 42, pp. 525-542.

P. Čisar and S. Maravić Čisar, "Methodological Frameworks of Digital Forensics", In Proceedings of the 9th IEEE International Symposium on Intelligent Systems and Informatics SISY 2011, 2011, pp. 343-347.

P. Čisar and S. Maravić Čisar, "General Directions of Development in Digital Forensics", Acta Technica Corviniensis, vol. 5, no. 2, pp. 87-91, 2012.

P. Čisar and J. Fodor, "Digital Image Forgery Identification", In Proceedings of International Scientific Conference "Archibald Reiss Days" 2015, vol. I, 2015, pp. 93-99.

J. Fridrich, D. Soukal and J. Lukáš, "Detection of Copy-Move Forgery in Digital Images," International J., vol. 3, pp. 652-663, 2003.

A. C. Popescu and H. Farid, "Exposing Digital Forgeries by Detecting Duplicated Image Regions", Technical Report TR2004-515, Dartmouth College, 2004.

S. J. Ryu, M. J. Lee and H. K. Lee, "Detection of Copy-rotate-move Forgery Using Zernike Noments", in Proceedings of Information Hiding (IH 2010), R. Böhme, P. W. L. Fong and R. Safavi-Naini, Eds., Berlin, Germany: Springer, 2010, Lect. Notes Comput. Sci., vol. 6387, pp. 51-65.

D. G. Lowe, "Distinctive Image Features from Scale-invariant Keypoints", Int. J. Comput. Vis., vol. 60, no. 2, pp. 91-110, Nov. 2004.

H. Bay, A. Ess, T. Tuytelaars and L. Van Gool, "Speeded up Robust Features (SURF)", Comput. Vis. Image Underst., vol. 110, no. 3, pp. 346-359, 2008.

E. Rublee, V. Rabaud, K. Konolige and G. Bradski, "ORB: An Efficient Alternative to SIFT or SURF," In Proceedings of International Conference on Computer Vision, Barcelona, Spain, 2011, pp. 2564-2571.

M. A. Fischler and R. C. Bolles, "Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography", Commun. ACM, vol. 24, no. 6, pp. 381-395, June 1981.

H. Farid, "Detecting Digital Forgeries Using Demosaicing Artifacts", IEEE Signal Process. Lett., vol. 13, no. 9, pp. 611-615, Sep. 2006.

T. Bianchi and A. Piva, "Image Forgery Localization via Block-grained Analysis of JPEG Artifacts", IEEE Trans. Inf. Forensics Secur., vol. 7, no. 3, pp. 1003-1017, Sep. 2012.

J. Lukás, J. Fridrich and M. Goljan, "Digital Camera Identification from Sensor Pattern Noise", IEEE Trans. Inf. Forensics Secur., vol. 1, no. 2, pp. 205-214, June 2006.

H. Farid, "Exposing Digital Forgeries from JPEG Ghosts", IEEE Trans. Inf. Forensics Secur., vol. 4, no. 1, pp. 154-160, Mar. 2009.

B. Mahdian and S. Saic, "Blind Authentication Using Periodic Properties of Interpolation", IEEE Trans. Inf. Forensics Secur., vol. 3, no. 3, pp. 529-538, Sept. 2008.

P. Yang, R. Ni and Y. Zhao, "Double JPEG Compression Detection by Exploring the Correlations in DCT Domain", arXiv preprint arXiv:1806.01571, June 2018.

M. K. Johnson and H. Farid, "Exposing Digital Forgeries Through Chromatic Aberration", In Proceedings of the 8th Workshop on Multimedia and Security (MM&Sec '06), New York, NY, USA: Association for Computing Machinery, 2006, pp. 48-55.

M. K. Johnson and H. Farid, "Exposing Digital Forgeries by Detecting Inconsistencies in Lighting", In Proceedings of the 7th Workshop on Multimedia and Security (MM&Sec '05), New York, NY, USA: Association for Computing Machinery, 2005, pp. 1-10.

C. Riess and E. Angelopoulou, "Physics-based Illuminant Color Estimation as an Image Semantics Clue", In Proceedings of the International Conference on Image Processing (ICIP), Cairo, Egypt, 2009, pp. 689-692.

E. Kee, J. F. O'Brien and H. Farid, "Exposing Photo Manipulation with Inconsistent Shadows", ACM Trans. Graph., vol. 32, no. 3, p. 28, June 2013.

M. Iuliani, G. Fabbri and A. Piva, "Image Splicing Detection Based on General Perspective Constraints", In Proceedings of the IEEE International Workshop on Information Forensics and Security (WIFS), Rome, Italy, 2015, pp. 1-6.

D. Afchar, V. Nozick, J. Yamagishi and I. Echizen, "MesoNet: a Compact Facial Video Forgery Detection Network", In Proceedings of the IEEE International Workshop on Information Forensics and Security (WIFS), Hong Kong, China, 2018, pp. 1-7.

Y. Wu, W. AbdAlmageed and P. Natarajan, "ManTra-Net: Manipulation Tracing Network for Detection and Localization of Image Forgeries with Anomalous Features", In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp. 9535-9544.

R. Salloum, Y. Ren and C.-C. J. Kuo, "Image Splicing Localization Using a Multi-task Fully Convolutional Network (MFCN)", J. Vis. Commun. Image Represent., vol. 51, pp. 201-209, 2018.

Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei and Z. Zhang, "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows", In Proceedings of IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada, 2021, pp. 9992-10002.

X. Hu, Z. Zhang, Z. Jiang, S. Chaudhuri, Z. Yang and R. Nevatia, "SPAN: Spatial Pyramid Attention Network for Image Manipulation Localization", in Computer Vision – ECCV 2020, Lecture Notes in Computer Science, vol. 12366, Cham, Switzerland: Springer International Publishing, 2020, pp. 312-328.

O. Ronneberger, P. Fischer and T. Brox, "U-Net: Convolutional Networks for Biomedical Image Segmentation", in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, N. Navab, J. Hornegger, W. Wells and A. Frangi, Eds., Lecture Notes in Computer Science, vol. 9351, Cham, Switzerland: Springer, 2015, pp. 234-241.

D. Cozzolino and L. Verdoliva, "Noiseprint: A CNN-based Camera Model Fingerprint", IEEE Trans. Inf. Forensics Secur., vol. 15, pp. 144-159, 2020.

A. Rössler, D. Cozzolino, L. Verdoliva, C. Riess, J. Thies and M. Niessner, "FaceForensics++: Learning to Detect Manipulated Facial Images", In Proceedings of IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South), 2019, pp. 1-11.

M. Barni, L. Bondi, N. Bonettini, P. Bestagini, A. Costanzo, M. Maggini, B. Tondi and S. Tubaro, "Aligned and Non-Aligned Double JPEG Detection Using Convolutional Neural Networks", J. Vis. Commun. Image Represent., vol. 49, pp. 153-163, 2017.

CASIA Image Tampering Detection Evaluation Database. [Online]. Available: http://forensics.idealtest.org/

Columbia Image Splicing Detection Evaluation Dataset. [Online]. Available: https://www.ee.columbia.edu/ln/dvmm/downloads/AuthSplicedDataSet/

D. Tralić, I. Župančić, S. Grgić and M. Grgić, "CoMoFoD – New Database for Copy-Move Forgery Detection", In Proceedings of the 55th International Symposium ELMAR2013, Zadar, Croatia, Sep. 2013, pp. 49-54.

J. Guo, J. Zhang, T. Chen and H. Liu, "Hierarchical Fine-Grained Image Forgery Detection and Localization", In Proceedings of IEEE/CVPR 2023, pp. 9457–9466.

Y. Liu, X. Zhu, X. Zhao and Y. Cao, "Adversarial Learning for Constrained Image Splicing Detection and Localization Based on Atrous Convolution," IEEE Trans. Inf. Forensics Secur., vol. 14, no. 10, pp. 2551-2566, Oct. 2019.

A. A. Solanke, "Explainable Digital Forensics AI: Towards Mitigating Distrust in AI-based Digital Forensics Analysis Using Interpretable Models", Forensic Sci. Int.: Digit. Investig., vol. 42, p. 301403, 2022.

H. Cheng, L. Niu, Z. Zhang and L. Ye, "Generalization Enhancement Strategy Based on Ensemble Learning for Open Domain Image Manipulation Detection", J. Vis. Commun. Image Represent., vol. 107, p. 104396, 2025.

W. Zheng, X. Ke and W. Guo, "Zero-shot 3D Anomaly Detection via Online Voter Mechanism", Neural Netw., vol. 187, p. 107398, 2025.

S. Usmani, S. Kumar and D. Sadhya, "Spatio-temporal Knowledge Distilled Video Vision Transformer (STKD-VViT) for Multimodal Deepfake Detection", Neurocomputing, vol. 620, p. 129256, 2025.

N. Xiao, Z. Wang, X. Sun and J. Miao, "A Novel Blockchain-based Digital Forensics Framework for Preserving Evidence and Enabling Investigation in Industrial Internet of Things", Alexandria Eng. J., vol. 86, pp. 631-643, 2024.

I. Amerini, M. Barni, S. Battiato, P. Bestagini, G. Boato, T. S. Bonaventura et al., "Deepfake Media Forensics: State of the Art and Challenges Ahead", arXiv preprint arXiv:2408.00388, Aug. 2024.

I. Vuković, P. Čisar, K. Kuk, M. Banđur and B. Popović, "Influence of Image Enhancement Techniques on Effectiveness of Unconstrained Face Detection and Identification", Elektronika ir Elektrotechnika, vol. 27, no. 5, pp. 49-58, 2021.

U. Samariya, S. D. Kamble, S. Singh et al., "A Survey on Copy-Move Image Forgery Detection Based on Deep-learning Techniques", Multimed. Tools Appl., vol. 84, pp. 30603-30662, 2024.

M. D. Ansari, S. P. Ghrera and V. Tyagi, "Pixel-based image forgery detection: A review", IETE J. Education, vol. 55, no. 1, pp. 40-46, 2024.

S. Singh and R. Kumar, "Image Forgery Detection: Comprehensive Review of Digital Forensics Approaches", J. Computat. Soc. Sci., vol. 7, pp. 1-39, 2024.

A. K. Jaiswal and R. Srivastava, "Detection of Copy-Move Forgery in Digital Image Using Multi-scale, Multi-stage Deep Learning Model", Neural Process. Lett., vol. 54, pp. 75-100, 2022.


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