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Encoder-decoder based convolutional neural networks for image forgery detection

  • 1169: Interdisciplinary Forensics: Government, Academia and Industry Interaction
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Abstract

Today, images editing software has greatly evolved, thanks to them that the semantic manipulation of images has become easier. On the other hand, the identification of these modifications becomes a very difficult task because the modified regions are not visually apparent. In this article, a new convolutional neural network method based on an encoder/decoder called Fals-Unet is proposed to locate the manipulated regions. The encoder of our method uses an architecture topologically identical to that of the Resnet50 method; its main goal is the exploitation of spatial maps to analyze the discriminating characteristics between the manipulated and non-manipulated regions. The decoding network learns the mapping from low-resolution feature maps to pixel-wise predictions for localizing the falsified regions. Finally, the predicted binary mask (0: falsify, 1: not falsify) is generated by the final layer (softmax). Experimental results on many public datasets CASIA, NIST’16, COVERAGE, and COMOD show that the proposed CNN-based model outperforms some methods.

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References

  1. Al-Qershi OM, Khoo BE (2013) Passive detection of copy-move forgery in digital images: state-of-the-art. Forensic Sci Int 231(1-3):284–295

    Article  Google Scholar 

  2. Amerini I, Ballan L, Caldelli R, Del Bimbo A, Serra G (2011) A sift-based forensic method for copy–move attack detection and transformation recovery. IEEE Trans Inf Forensics Secur 6(3):1099–1110

    Article  Google Scholar 

  3. Ansari MD, Ghrera SP, Tyagi V (2014) Pixel-based image forgery detection: a review. IETE J Educ 55(1):40–46

    Article  Google Scholar 

  4. Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495

    Article  Google Scholar 

  5. Bappy JH, Roy-Chowdhury AK, Bunk J, Nataraj L, Manjunath B (2017) Exploiting spatial structure for localizing manipulated image regions. In: Proceedings of the IEEE international conference on computer vision, pp 4970–4979

  6. Bappy JH, Simons C, Nataraj L, Manjunath B, Roy-Chowdhury AK (2019) Hybrid lstm and encoder–decoder architecture for detection of image forgeries. IEEE Trans Image Process 28(7):3286–3300

    Article  MathSciNet  MATH  Google Scholar 

  7. Bayar B, Stamm MC (2016) A deep learning approach to universal image manipulation detection using a new convolutional layer. In: Proceedings of the 4th ACM workshop on information hiding and multimedia security, pp 5–10

  8. Bayar B, Stamm MC (2017) Design principles of convolutional neural networks for multimedia forensics. Electronic Imaging 2017(7):77–86

    Article  Google Scholar 

  9. Bayar B, Stamm MC (2017) On the robustness of constrained convolutional neural networks to jpeg post-compression for image resampling detection. In: 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 2152–2156

  10. Bharati A, Singh R, Vatsa M, Bowyer KW (2016) Detecting facial retouching using supervised deep learning. IEEE Transactions on Information Forensics and Security 11(9):1903–1913

    Article  Google Scholar 

  11. Bianchi T, De Rosa A, Piva A (2011) Improved dct coefficient analysis for forgery localization in jpeg images. In: 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 2444–2447

  12. Bianchi T, Piva A (2012) Image forgery localization via block-grained analysis of jpeg artifacts. IEEE Transactions on Information Forensics and Security 7(3):1003–1017

    Article  Google Scholar 

  13. Boughorbel S, Jarray F, El-Anbari M (2017) Optimal classifier for imbalanced data using matthews correlation coefficient metric. PloS one 12(6)

  14. Buccoli M, Bestagini P, Zanoni M, Sarti A, Tubaro S (2014) Unsupervised feature learning for bootleg detection using deep learning architectures. In: 2014 IEEE international workshop on information forensics and security (WIFS). IEEE, pp 131–136

  15. Bunk J, Bappy JH, Mohammed TM, Nataraj L, Flenner A, Manjunath B, Chandrasekaran S, Roy-Chowdhury AK, Peterson L (2017) Detection and localization of image forgeries using resampling features and deep learning. In: 2017 IEEE conference on computer vision and pattern recognition workshops (CVPRW). IEEE, pp 1881–1889

  16. Cai W, Wei Z (2020) Piigan: generative adversarial networks for pluralistic image inpainting. IEEE Access 8:48451–48463

    Article  Google Scholar 

  17. Chang IC, Yu JC, Chang CC (2013) A forgery detection algorithm for exemplar-based inpainting images using multi-region relation. Image Vis Comput 31(1):57–71

    Article  MathSciNet  Google Scholar 

  18. Chawla NV, Japkowicz N, Kotcz A (2004) Special issue on learning from imbalanced data sets. ACM SIGKDD Explorations Newsletter 6(1):1–6

    Article  Google Scholar 

  19. Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 40(4):834–848

    Article  Google Scholar 

  20. Chollet F, et al. (2015) Keras, github. Github repository, https://github.com/fchollet/keras

  21. Cozzolino D, Poggi G, Verdoliva L (2015) Efficient dense-field copy–move forgery detection. IEEE Transactions on Information Forensics and Security 10 (11):2284–2297

    Article  Google Scholar 

  22. Dirik AE, Memon N (2009) Image tamper detection based on demosaicing artifacts. In: 2009 16th IEEE international conference on image processing (ICIP). IEEE, pp 1497–1500

  23. Dong J, Wang W (2011) Casia tampered image detection evaluation (tide) database v1.0 and v2.0. http://forensics.idealtest.org/

  24. Farid H (1999) Detecting digital forgeries using bispectral analysis [R]. MIT, Cambridge, USA. Perceptual Science Group

    Google Scholar 

  25. Farid H (2009) Exposing digital forgeries from jpeg ghosts. IEEE Trans Inf Forensics Secur 4(1):154–160

    Article  Google Scholar 

  26. Feng X, Cox IJ, Doërr G (2011) An energy-based method for the forensic detection of re-sampled images. In: 2011 IEEE international conference on multimedia and expo. IEEE, pp 1–6

  27. Feng X, Cox IJ, Doerr G (2012) Normalized energy density-based forensic detection of resampled images. IEEE Trans Multimed 14(3):536–545

    Article  Google Scholar 

  28. Ferrara P, Bianchi T, De Rosa A, Piva A (2012) Image forgery localization via fine-grained analysis of cfa artifacts. IEEE Transactions on Information Forensics and Security 7(5):1566–1577

    Article  Google Scholar 

  29. Fillion C, Sharma G (2010) Detecting content adaptive scaling of images for forensic applications. In: Media forensics and security II. International Society for Optics and Photonics, vol 7541, p 75410z

  30. Fu D, Shi YQ, Su W (2006) Detection of image splicing based on hilbert-huang transform and moments of characteristic functions with wavelet decomposition. In: International workshop on digital watermarking. Springer, pp 177–187

  31. Gao S, Liao X, Liu X (2019) Real-time detecting one specific tampering operation in multiple operator chains. J Real-Time Image Proc 16(3):741–750

    Article  Google Scholar 

  32. Golestaneh SA, Chandler DM (2014) Algorithm for jpeg artifact reduction via local edge regeneration. Journal of Electronic Imaging 23(1):013018

    Article  Google Scholar 

  33. Guillemot C, Le Meur O (2013) Image inpainting: overview and recent advances. IEEE Signal Proc Mag 31(1):127–144

    Article  Google Scholar 

  34. He H, Garcia EA (2009) Learning from imbalanced data. IEEE Trans Knowl Data Eng 21(9):1263–1284

    Article  Google Scholar 

  35. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  36. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167

  37. Jaberi M, Bebis G, Hussain M, Muhammad G (2014) Accurate and robust localization of duplicated region in copy–move image forgery. Mach Vis Appl 25(2):451–475

    Article  Google Scholar 

  38. Johnson MK, Farid H (2007) Exposing digital forgeries through specular highlights on the eye. In: International workshop on information hiding. Springer, pp 311–325

  39. Kakar P, Sudha N (2012) Exposing postprocessed copy–paste forgeries through transform-invariant features. IEEE Transactions on Information Forensics and Security 7(3):1018–1028

    Article  Google Scholar 

  40. Kendall A, Badrinarayanan V, Cipolla R (2015) Bayesian segnet: model uncertainty in deep convolutional encoder-decoder architectures for scene understanding. arXiv:1511.02680

  41. Kwon Y, Kim KI, Tompkin J, Kim JH, Theobalt C (2015) Efficient learning of image super-resolution and compression artifact removal with semi-local gaussian processes. IEEE Trans Pattern Anal Mach Intell 37(9):1792–1805

    Article  Google Scholar 

  42. Li J, Li X, Yang B, Sun X (2014) Segmentation-based image copy-move forgery detection scheme. IEEE Transactions on Information Forensics and Security 10(3):507–518

    Google Scholar 

  43. Li W, Yuan Y, Yu N (2009) Passive detection of doctored jpeg image via block artifact grid extraction. Signal Process 89(9):1821–1829

    Article  MATH  Google Scholar 

  44. Liang Z, Yang G, Ding X, Li L (2015) An efficient forgery detection algorithm for object removal by exemplar-based image inpainting. J Vis Commun Image Represent 30:75–85

    Article  Google Scholar 

  45. Liao X, Li K, Zhu X, Liu KJ (2020) Robust detection of image operator chain with two-stream convolutional neural network. IEEE Journal of Selected Topics in Signal Processing 14(5):955–968

    Article  Google Scholar 

  46. Lin Z, He J, Tang X, Tang CK (2009) Fast, automatic and fine-grained tampered jpeg image detection via dct coefficient analysis. Pattern Recogn 42(11):2492–2501

    Article  MATH  Google Scholar 

  47. Liu Q, Chen Z (2014) Improved approaches with calibrated neighboring joint density to steganalysis and seam-carved forgery detection in jpeg images. ACM Trans Intell Syst Technol (TIST) 5(4):1–30

    Google Scholar 

  48. Luo W, Huang J, Qiu G (2006) Robust detection of region-duplication forgery in digital image. In: 18th international conference on pattern recognition (ICPR’06). IEEE, vol 4, pp 746–749

  49. Luo W, Huang J, Qiu G (2010) Jpeg error analysis and its applications to digital image forensics. IEEE Trans Inf Forensics Secur 5(3):480–491

    Article  Google Scholar 

  50. Mahdian B, Saic S (2008) Blind authentication using periodic properties of interpolation. IEEE Trans Inf Forensics Secur 3(3):529–538

    Article  Google Scholar 

  51. Manu V, Mehtre B (2015) Visual artifacts based image splicing detection in uncompressed images. In: 2015 IEEE international conference on computer graphics, vision and information security (CGVIS). IEEE, pp 145–150

  52. Mohammed TM, Bunk J, Nataraj L, Bappy JH, Flenner A, Manjunath B, Chandrasekaran S, Roy-Chowdhury AK, Peterson LA (2018) Boosting image forgery detection using resampling features and copy-move analysis. Electronic Imaging 2018(7):118–1

    Google Scholar 

  53. Muhammad G, Al-Hammadi MH, Hussain M, Bebis G (2014) Image forgery detection using steerable pyramid transform and local binary pattern. Mach Vis Appl 25(4):985–995

    Article  Google Scholar 

  54. Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th international conference on machine learning (ICML-10), pp 807–814

  55. Nataraj L, Sarkar A, Manjunath BS (2010) Improving re-sampling detection by adding noise. In: Media forensics and security II. International Society for Optics and Photonics, vol 7541, p 75410i

  56. Nguyen HH, Tieu TND, Nguyen-Son HQ, Nozick V, Yamagishi J, Echizen I (2018) Modular convolutional neural network for discriminating between computer-generated images and photographic images. In: Proceedings of the 13th international conference on availability, reliability and security, pp 1–10

  57. Nist (2016) Nimble. https://www.nist.gov/sites/default/files/documents/2016/11/30/shouldibelieveornot.pdf

  58. Pinheiro PO, Lin TY, Collobert R, Dollár P (2016) Learning to refine object segments. In: European conference on computer vision. Springer, pp 75–91

  59. Popescu AC, Farid H (2004) Exposing digital forgeries by detecting duplicated image regions. Department of Computer Science, Dartmouth College, Tech Rep TR2004-515, pp 1–11

  60. Popescu AC, Farid H (2005) Exposing digital forgeries by detecting traces of resampling. IEEE Trans Signal Process 53(2):758–767

    Article  MathSciNet  MATH  Google Scholar 

  61. Qian Y, Dong J, Wang W, Tan T (2015) Deep learning for steganalysis via convolutional neural networks. In: Media watermarking, security, and forensics 2015. International Society for Optics and Photonics, vol 9409, p 94090j

  62. Rahmouni N, Nozick V, Yamagishi J, Echizen I (2017) Distinguishing computer graphics from natural images using convolution neural networks. In: 2017 IEEE workshop on information forensics and security (WIFS). IEEE, pp 1–6

  63. Rao Y, Ni J (2016) A deep learning approach to detection of splicing and copy-move forgeries in images. In: 2016 IEEE international workshop on information forensics and security (WIFS). IEEE, pp 1–6

  64. Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 234–241

  65. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, et al. (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211–252

    Article  MathSciNet  Google Scholar 

  66. Ryu SJ, Lee HK (2014) Estimation of linear transformation by analyzing the periodicity of interpolation. Pattern Recogn Lett 36:89–99

    Article  Google Scholar 

  67. Salloum R, Ren Y, Kuo CCJ (2018) Image splicing localization using a multi-task fully convolutional network (mfcn). J Vis Commun Image Represent 51:201–209

    Article  Google Scholar 

  68. Sarkar A, Nataraj L, Manjunath BS (2009) Detection of seam carving and localization of seam insertions in digital images. In: Proceedings of the 11th ACM workshop on Multimedia and security, pp 107–116

  69. Shelhamer E, Long J, Darrell T (2017) Fully convolutional networks for semantic segmentation. IEEE Ann Hist Comput (04):640–651

  70. Shi YQ, Chen C, Chen W (2007) A natural image model approach to splicing detection. In: Proceedings of the 9th workshop on Multimedia & security, pp 51–62

  71. Tralic D, Zupancic I, Grgic S, Grgic M (2013) Comofod—new database for copy-move forgery detection. In: Proceedings ELMAR-2013. IEEE, pp 49–54

  72. Verdoliva L, Cozzolino D, Poggi G (2014) A feature-based approach for image tampering detection and localization. In: 2014 IEEE international workshop on information forensics and security (WIFS). IEEE, pp 149–154

  73. Wang Z, Zou C, Cai W (2020) Small sample classification of hyperspectral remote sensing images based on sequential joint deeping learning model. IEEE Access 8:71353–71363

    Article  Google Scholar 

  74. Wen B, Zhu Y, Subramanian R, Ng TT, Shen X, Winkler S (2016) Coverage—a novel database for copy-move forgery detection. In: 2016 IEEE international conference on image processing (ICIP). IEEE, pp 161–165

  75. Wu Q, Sun SJ, Zhu W, Li GH, Tu D (2008) Detection of digital doctoring in exemplar-based inpainted images. In: 2008 international conference on machine learning and cybernetics. IEEE, vol 3, pp 1222–1226

  76. Xiao B, Wei Y, Bi X, Li W, Ma J (2020) Image splicing forgery detection combining coarse to refined convolutional neural network and adaptive clustering. Inf Sci 511:172–191

    Article  MathSciNet  Google Scholar 

  77. Ye S, Sun Q, Chang EC (2007) Detecting digital image forgeries by measuring inconsistencies of blocking artifact. In: 2007 IEEE international conference on multimedia and expo. IEEE, pp 12–15

  78. You H, Tian S, Yu L, Lv Y (2019) Pixel-level remote sensing image recognition based on bidirectional word vectors. IEEE Trans Geosci Remote Sens 58(2):1281–1293

    Article  Google Scholar 

  79. Zhang R, Ni J (2020) A dense u-net with cross-layer intersection for detection and localization of image forgery. In: ICASSP 2020-2020 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 2982–2986

  80. Zhang Y, Goh J, Win LL, Thing VL (2016) Image region forgery detection: a deep learning approach. SG-CRC 2016:1–11

    Google Scholar 

  81. Zheng S, Jayasumana S, Romera-Paredes B, Vineet V, Su Z, Du D, Huang C, Torr PH (2015) Conditional random fields as recurrent neural networks. In: Proceedings of the IEEE international conference on computer vision, pp 1529–1537

  82. Zhou B, Lapedriza A, Xiao J, Torralba A, Oliva A (2014) Learning deep features for scene recognition using places database. In: Advances in neural information processing systems, pp 487–495

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  1. LRIT Associated Unit to the CNRST-URAC N29, Faculty of Sciences, Mohammed V University in Rabat, B.P. 1014 RP, Rabat IT Center, Rabat, Morocco

    Fatima Zahra El Biach, Imad Iala, Hicham Laanaya & Khalid Minaoui

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  1. Fatima Zahra El Biach
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El Biach, F.Z., Iala, I., Laanaya, H. et al. Encoder-decoder based convolutional neural networks for image forgery detection. Multimed Tools Appl 81, 22611–22628 (2022). https://doi.org/10.1007/s11042-020-10158-3

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