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Image Forgery Detection Using Noise and Edge Weighted Local Texture Features

ASGHAR, K. See more information about ASGHAR, K. on SCOPUS See more information about ASGHAR, K. on IEEExplore See more information about ASGHAR, K. on Web of Science, SADDIQUE, M. See more information about  SADDIQUE, M. on SCOPUS See more information about  SADDIQUE, M. on SCOPUS See more information about SADDIQUE, M. on Web of Science, HUSSAIN, M. See more information about  HUSSAIN, M. on SCOPUS See more information about  HUSSAIN, M. on SCOPUS See more information about HUSSAIN, M. on Web of Science, BEBIS, G. See more information about  BEBIS, G. on SCOPUS See more information about  BEBIS, G. on SCOPUS See more information about BEBIS, G. on Web of Science, HABIB, Z. See more information about HABIB, Z. on SCOPUS See more information about HABIB, Z. on SCOPUS See more information about HABIB, Z. on Web of Science
 
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Author keywords
artificial intelligence, forgery, Fourier transforms, machine learning, pattern recognition

References keywords
image(54), detection(35), forgery(28), noise(20), forensics(18), information(15), features(14), security(13), processing(13), multimedia(12)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2022-02-28
Volume 22, Issue 1, Year 2022, On page(s): 57 - 69
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2022.01007
Web of Science Accession Number: 000762769600006
SCOPUS ID: 85126766756

Abstract
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Image forgery detection is important for sensitive domains such as courts of law. The main challenge is to develop a robust model that is sensitive to tampering traces. Existing techniques perform well on a limited dataset but do not generalize well across the datasets. Moreover, these techniques cannot reliably detect tampering that distorts the texture pattern of the image. The noise patterns remain consistent throughout a digital image if its contents are not altered. Based on this hypothesis, a robust descriptor FFT-DRLBP (Fast Fourier Transformation - Discriminative Robust Local Binary Patterns) is introduced, which first estimates noise patterns using FFT and encodes the discrepancies in noise patterns using DRLBP. Features extracted are passed to Support Vector Machine (SVM) for deciding whether the image is authentic or tampered. Intensive experiments are performed on benchmark datasets to validate the performance of the method. It achieved an accuracy of 99.21% on the combination of two challenging datasets. The comparison shows that it outperforms state-of-the-art methods and is vigorous to image forgery attacks even in the presence of various post-processing operations. The performance of the method is also validated using cross-dataset experiments, which ensures its robustness and generalization.


References | Cited By  «-- Click to see who has cited this paper

[1] R. Pandey, S. Singh, and K. Shukla, "Passive forensics in image and video using noise features: A review," Digital Investigation, vol. 19, pp. 1-28, 2016.
[CrossRef] [Web of Science Times Cited 32]


[2] H. Gou, A. Swaminathan, and M. Wu, "Noise features for image tampering detection and steganalysis," in IEEE International Conference on Image Processing, San Antonio, Texas, USA, 2007, pp. 97-100.
[CrossRef]


[3] J. Fan, H. Cao, and A. C. Kot, "Estimating EXIF parameters based on noise features for image manipulation detection," IEEE Transactions on Information Forensics and Security, vol. 8, pp. 608-618, 2013.
[CrossRef] [Web of Science Times Cited 27]


[4] Y. Ke, C. Zhang, M. Qiang, Z. Weidong and Shuguang, "Detecting image forgery based on noise estimation," International Journal of Multimedia and Ubiquitous Engineering, vol. 9, pp. 325-336, 2014.
[CrossRef]


[5] C. M. Pun, B. Liu, and X. C. Yuan, "Multi-scale noise estimation for image splicing forgery detection," Journal of Visual Communication and Image Representation, vol. 38, pp. 195-206, 2016.
[CrossRef] [Web of Science Times Cited 45]


[6] J. Lukas, J. Fridrich, and M. Goljan, "Detecting digital image forgeries using sensor pattern noise," in Security, Steganography, and Watermarking of Multimedia Contents VIII, 2006, p. 60720Y.
[CrossRef] [Web of Science Times Cited 120]


[7] C.-C. Hsu, T.-Y. Hung, C.-W. Lin, and C.-T. Hsu, "Video forgery detection using correlation of noise residue," in 2008 IEEE 10th workshop on multimedia signal processing, 2008, pp. 170-174

[8] X. Pan, X. Zhang, and S. Lyu, "Exposing image forgery with blind noise estimation," in Proceedings of the thirteenth ACM multimedia workshop on Multimedia and security, 2011, pp. 15-20

[9] C. Liu, R. Szeliski, S. B. Kang, C. L. Zitnick, W. T. Freeman, "Automatic estimation and removal of noise from a single image," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, pp. 299-314, 2007.
[CrossRef] [Web of Science Times Cited 338]


[10] C. Liu, W. T. Freeman, R. Szeliski, and S. B. Kang, "Noise estimation from a single image," in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), 2006, pp. 901-908.
[CrossRef]


[11] B. Goyal, A. Dogra, S. Agrawal, B. Sohi, and A. Sharma, "Image denoising review: From classical to state-of-the-art approaches," Information Fusion, vol. 55, pp. 220-244, 2020.
[CrossRef] [Web of Science Times Cited 159]


[12] [12] A. Khurshid, G. Ghulam , S. Mubbashar, and H. Zulfiqar, "Automatic enhancement of digital images using Cubic Bézier Curve and Fourier transformation," Malaysian Journal of Computer Science, vol. 30, pp. 300-310, 2017.
[CrossRef]


[13] A. Satpathy, X. Jiang, and H. L. Eng, "LBP-based edge-texture features for object recognition," IEEE Transactions on Image Processing, vol. 23, pp. 1953-1964, 2014.
[CrossRef] [Web of Science Times Cited 177]


[14] A. Khurshid, H. Zulfiqar, and H. Muhammad, "Copy-move and splicing image forgery detection and localization techniques: a review," Australian Journal of Forensic Sciences, vol. 49, pp. 281-307, 2017.
[CrossRef] [Web of Science Times Cited 66]


[15] E.-S. M. El-Alfy and M. A. Qureshi, "Robust content authentication of gray and color images using lbp-dct markov-based features," Multimedia Tools and Applications, vol. 76, pp. 1-22, 2016.
[CrossRef] [Web of Science Times Cited 7]


[16] W. Luo, J. Huang, and G. Qiu, "Robust detection of region-duplication forgery in digital image," in 18th International Conference on Pattern Recognition,( ICPR), Hong Kong, 2006, pp. 746-749.
[CrossRef]


[17] S. Bayram, H. Sencar, and N. Memon, "An efficient and robust method for detecting copy-move forgery," in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Taipei, Taiwan, 2009, pp. 1053-1056.
[CrossRef] [Web of Science Times Cited 266]


[18] B. Mahdian and S. Saic, "Blind methods for detecting image fakery," IEEE Aerospace and Electronic Systems Magazine, vol. 25, pp. 18-24, 2010.
[CrossRef]


[19] Y. Wu, Y. Deng, H. Duan, and L. Zhou, "Dual tree complex wavelet transform approach to copy-rotate-move forgery detection," Science China Information Sciences, vol. 57, pp. 1-12, 2014.
[CrossRef] [Web of Science Times Cited 2]


[20] F. Peng, Y.-Y. Nie, and M. Long, "A complete passive blind image copy-move forensics scheme based on compound statistics features," vol. 212, pp. e21-e25, 2011.
[CrossRef] [Web of Science Times Cited 24]


[21] R. C. Pandey, S. K. Singh, and K. K. Shukla, "A passive forensic method for video: Exposing dynamic object removal and frame duplication in the digital video using sensor noise features," Journal of Intelligent Fuzzy Systems, vol. 32, pp. 3339-3353, 2017.
[CrossRef] [Web of Science Times Cited 5]


[22] A. C. Popescu and H. Farid, "Statistical tools for digital forensics," in Information Hiding, 2004, pp. 395-407.
[CrossRef]


[23] H. Gou, A. Swaminathan, and M. Wu, "Noise features for image tampering detection and steganalysis," in 2007 IEEE International Conference on Image Processing, 2007, pp. VI-97-VI-100

[24] B. Mahdian and S. Saic, "Using noise inconsistencies for blind image forensics," Image and Vision Computing, vol. 27, pp. 1497-1503, 2009.
[CrossRef] [Web of Science Times Cited 212]


[25] X. Kang, Y. Li, Z. Qu, and J. Huang, "Enhancing source camera identification performance with a camera reference phase sensor pattern noise," IEEE Transactions on Information Forensics and Security, vol. 7, pp. 393-402, 2012.
[CrossRef] [Web of Science Times Cited 143]


[26] M. C. Stamm and K. R. Liu, "Forensic detection of image manipulation using statistical intrinsic fingerprints," IEEE Transactions on Information Forensics and Security, vol. 5, pp. 492-506, 2010.
[CrossRef] [Web of Science Times Cited 212]


[27] B. Liu, C. M. Pun, and X. C. Yuan, "Digital image forgery detection using JPEG features and local noise discrepancies," The Scientific World Journal, vol. 14, pp. 1-12, 2014.
[CrossRef] [Web of Science Times Cited 10]


[28] G. Chierchia, G. Poggi, C. Sansone, and L. Verdoliva, "A Bayesian-MRF approach for PRNU-based image forgery detection," IEEE Transactions on Information Forensics and Security, vol. 9, pp. 554-567, 2014.
[CrossRef] [Web of Science Times Cited 114]


[29] H. Y. Yang, Y. Niu, L. Jiao, Y. Liu, X. Wang, and Z. Zhou, "Robust copy-move forgery detection based on multi-granularity Superpixels matching," Multimedia Tools and Applications, pp. 1-27, 2017.
[CrossRef] [Web of Science Times Cited 9]


[30] L. Wang and S.-i. Kamata, "Forgery image detection via mask filter banks based CNN," in 10th International Conference on Graphics and Image Processing, Chengdu, China, 2019, pp. 1-6.
[CrossRef] [Web of Science Record]


[31] K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in IEEE conference on computer vision and pattern recognition, Las Vegas, Nevada, USA, 2016, pp. 770-778.
[CrossRef] [Web of Science Times Cited 58759]


[32] V. Mall, K. Bhatt, S. K. Mitra, and A. K. Roy, "Exposing structural tampering in digital images," in 2012 IEEE International Conference on Signal Processing, Computing and Control, 2012, pp. 1-6.
[CrossRef]


[33] G. Gilanie, U. I. Bajwa, M. M. Waraich, Z. Habib, H. Ullah, and M. Nasir, "Classification of normal and abnormal brain MRI slices using Gabor texture and support vector machines," Signal, Image and Video Processing, vol. 12, pp. 479-487, 2018.
[CrossRef] [Web of Science Times Cited 19]


[34] G. Gilanie, N. Nasir, U. I. Bajwa, and H. Ullah, "RiceNet: convolutional neural networks-based model to classify Pakistani grown rice seed types," Multimedia Systems, pp. 1-9, 2021.
[CrossRef] [Web of Science Times Cited 12]


[35] M. Saddique, K. Asghar, U. I. Bajwa, M. Hussain, H. A. Aboalsamh, and Z. Habib, "Classification of authentic and tampered video using motion residual and parasitic layers," IEEE Access, vol. 8, pp. 56782-56797, 2020.
[CrossRef] [Web of Science Times Cited 12]


[36] R.-E. Precup, T.-A. Teban, A. Albu, A.-B. Borlea, I. A. Zamfirache, and E. M. Petriu, "Evolving fuzzy models for prosthetic hand myoelectric-based control," IEEE Transactions on Instrumentation and Measurement, vol. 69, pp. 4625-4636, 2020.
[CrossRef] [Web of Science Times Cited 128]


[37] I.-D. Borlea, R.-E. Precup, A.-B. Borlea, and D. Iercan, "A unified form of fuzzy C-means and K-means algorithms and its partitional implementation," Knowledge-Based Systems, vol. 214, p. 106731, 2021.
[CrossRef] [Web of Science Times Cited 102]


[38] H. Gou, A. Swaminathan, and M. Wu, "Intrinsic sensor noise features for forensic analysis on scanners and scanned images," IEEE Transactions on Information Forensics Security, vol. 4, pp. 476-491, 2009.
[CrossRef] [Web of Science Times Cited 45]


[39] X. Sun, Y. Li, S. Niu, and Y. Huang, "The detecting system of image forgeries with noise features and EXIF information," Journal of Systems Science and Complexity, vol. 28, pp. 1164-1176, 2015.
[CrossRef] [Web of Science Times Cited 6]


[40] C. C. Hsu, T. Y. Hung, C. W. Lin, and C. T. Hsu, "Video forgery detection using correlation of noise residue," in IEEE 10th Workshop on Multimedia Signal Processing, 2008, pp. 170-174.
[CrossRef] [Web of Science Times Cited 110]


[41] K. Asghar, X. Sun, P. L. Rosin, M. Saddique, M. Hussain, and Z. Habib, "Edge-texture feature-based image forgery detection with cross-dataset evaluation," Machine Vision and Applications, vol. 30, pp. 1243-1262, 2019.
[CrossRef] [Web of Science Times Cited 13]


[42] M. Saddique, K. Asghar, U. I. Bajwa, M. Hussain, and Z. Habib, "Spatial video forgery detection and localization using texture analysis of consecutive frames," Advances in Electrical and Computer Engineering, vol. 19, pp. 97-108, 2019.
[CrossRef] [Full Text] [Web of Science Times Cited 17]


[43] N. Kanwal, A. Girdhar, L. Kaur, and J. S. Bhullar, "Detection of digital image forgery using fast fourier transform and local features," in 2019 International Conference on Automation, Computational and Technology Management (ICACTM), 2019, pp. 262-267.
[CrossRef]


[44] J. Dong and W. Wang, "CASIA image tampering detection evaluation databases," in Signal and Information Processing (ChinaSIP), Beijing, China, 2011, pp. 422-426.
[CrossRef]


[45] Christlein, V. Riess, C. Jordan, and J. Angelopoulou, "An evaluation of popular copy-move forgery detection approaches," IEEE Transactions on Information Forensics and Security, vol. 7, pp. 1841-1854, 2012.
[CrossRef] [Web of Science Times Cited 456]


[46] D. Cozzolino, G. Poggi, and L. Verdoliva, "Efficient dense-field copy-move forgery detection," IEEE Transactions on Information Forensics and Security, vol. 10, pp. 2284-2297, 2015.
[CrossRef] [Web of Science Times Cited 229]


[47] M. Zampoglou and R. Bouwmeester, The Deutsche Welle Image Forensics Dataset [Online]. Available: https://revealproject.eu/the-deutsche-welle-image-forensics-dataset/

[48] I. Amerini, L. Ballan, R. Caldelli, A. Del Bimbo, and G. Serra, "A sift-based forensic method for copy-move attack detection and transformation recovery," IEEE Transactions on Information Forensics and Security, vol. 6, pp. 1099-1110, 2011.
[CrossRef] [Web of Science Times Cited 594]


[49] V. Christlein, C. Riess, J. Jordan, C. Riess, and E. Angelopoulou, "An evaluation of popular copy-move forgery detection approaches," IEEE Transactions on Information Forensics and Security, vol. 7, pp. 1841-1854, 2012.
[CrossRef] [Web of Science Times Cited 17]


[50] E. Silva, T. Carvalho, A. Ferreira, and A. Rocha, "Going deeper into copy-move forgery detection: Exploring image telltales via multi-scale analysis and voting processes," Journal of Visual Communication and Image Representation, vol. 29, pp. 16-32, 2015.
[CrossRef] [Web of Science Times Cited 150]


[51] [51] G. Cattaneo, G. Roscigno, and U. F. Petrillo, "Improving the experimental analysis of tampered image detection algorithms for biometric systems," Pattern Recognition Letters, vol. 113, pp. 93-101, 2017.
[CrossRef] [Web of Science Times Cited 3]


[52] D. Cozzolino, D. Gragnaniello, and L. Verdoliva, "Image forgery localization through the fusion of camera-based, feature-based and pixel-based techniques," in IEEE International Conference on Image Processing (ICIP), 2014, pp. 5302-5306.
[CrossRef]


[53] M. Zampoglou, S. Papadopoulos, and Y. Kompatsiaris, "Detecting image splicing in the wild (web)," in IEEE International Conference on Multimedia & Expo Workshops (ICMEW), 2015, pp. 1-6.
[CrossRef]


[54] M. Sokolova, N. Japkowicz, and S. Szpakowicz, "Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation," in Australasian joint conference on artificial intelligence, Berlin, Germany 2006, pp. 1015-1021.
[CrossRef]


[55] R. A. Melter, "Some characterizations of city block distance," vol. 6, pp. 235-240, 1987.
[CrossRef] [Web of Science Times Cited 23]


[56] L. Bai, A. Velichko, and B. W. Drinkwater, "Ultrasonic characterization of crack-like defects using scattering matrix similarity metrics," IEEE transactions on ultrasonics, ferroelectrics, and frequency control, vol. 62, pp. 545-559, 2015.
[CrossRef] [Web of Science Times Cited 42]


[57] J. Y. Hesterman, L. Caucci, M. A. Kupinski, H. H. Barrett, and L. R. Furenlid, "Maximum-likelihood estimation with a contracting-grid search algorithm," IEEE transactions on nuclear science, vol. 57, pp. 1077-1084, 2010.
[CrossRef] [Web of Science Times Cited 90]


[58] J. Byun, H. A. Patel, and C. T. Yavuz, "Magnetic BaFe12O19 nanofiber filter for effective separation of Fe3O4 nanoparticles and removal of arsenic," Journal of nanoparticle research, vol. 16, p. 2787, 2014.
[CrossRef] [Web of Science Times Cited 8]


[59] D. Tralic, I. Zupancic, S. Grgic, and M. Grgic, "CoMoFoD-New database for copy-move forgery detection," in 55th ELMAR International Symposium, Zadar, Croatia, 2013, pp. 49-54.
[CrossRef]


[60] B. Mahdian, S. J. I. Saic, "Using noise inconsistencies for blind image forensics," Image and Vision Computing, vol. 27, pp. 1497-1503, 2009.
[CrossRef] [Web of Science Times Cited 212]


[61] X. Pan, X. Zhang, and S. Lyu, "Exposing image splicing with inconsistent local noise variances," in IEEE International Conference on Computational Photography (ICCP), 2012, pp. 1-10.
[CrossRef]


[62] A. Alahmadi, M. Hussain, H. Aboalsamh, G. Muhammad, G. Bebis, and H. Mathkour, "Passive detection of image forgery using DCT and local binary pattern," Signal, Image and Video Processing, vol. 11, pp. 81-88, 2017.
[CrossRef] [Web of Science Times Cited 66]


[63] X. Shen, Z. Shi, and H. Chen, "Splicing image forgery detection using textural features based on the grey level co-occurrence matrices," IET Image Processing, vol. 11, pp. 44-53, 2016.
[CrossRef] [Web of Science Times Cited 42]


[64] J. Goh and V. L. Thing, "A hybrid evolutionary algorithm for feature and ensemble selection in image tampering detection," International Journal of Electronic Security and Digital Forensics, vol. 7, pp. 76-104, 2015.
[CrossRef] [Web of Science Times Cited 16]


[65] M. Hussain, S. Qasem, G. Bebis, G. Muhammad, H. Aboalsamh, and H. Mathkour, "Evaluation of image forgery detection using multi-scale weber local descriptors," International Journal on Artificial Intelligence Tools, vol. 24, pp. 1-28, 2015.
[CrossRef] [Web of Science Times Cited 30]


[66] G. Muhammad, M. Al-Hammadi, M. Hussain, and G. Bebis, "Image forgery detection using steerable pyramid transform and local binary pattern," Machine Vision and Applications, vol. 25, pp. 985-995, 2014.
[CrossRef]


[67] Y. Rao and J. Ni, "A deep learning approach to detection of splicing and copy-move forgeries in images," in IEEE International Workshop on Information Forensics and Security (WIFS), 2016, pp. 1-6.
[CrossRef]


[68] N. T. Pham, J.-W. Lee, G.-R. Kwon, and C.-S. Park, "Efficient image splicing detection algorithm based on markov features," Multimedia Tools and Applications, pp. 1-15, 2018.
[CrossRef] [Web of Science Times Cited 17]


[69] H. A. Jalab, T. Subramaniam, R. W. Ibrahim, H. Kahtan, and N. F. M. Noor, "New Texture Descriptor Based on Modified Fractional Entropy for Digital Image Splicing Forgery Detection," Entropy, vol. 21, p. 371, 2019.
[CrossRef] [Web of Science Times Cited 20]


[70] F. M. Al_Azrak, A. Sedik, M. I. Dessowky, G. M. El Banby, A. A. Khalaf, A. S. Elkorany, and F. E. A. El-Samie, "An efficient method for image forgery detection based on trigonometric transforms and deep learning," Multimedia Tools and Applications, pp. 1-23, 2020.
[CrossRef] [Web of Science Times Cited 26]


[71] G. Cattaneo, G. Roscigno, and U. F. Petrillo, "Improving the experimental analysis of tampered image detection algorithms for biometric systems," Pattern Recognition Letters, vol. 113, pp. 93-101, 2018.
[CrossRef] [Web of Science Times Cited 3]




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Web of Science® Citations for all references: 63,235 TCR
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