Abstract
The increasing prevalence of digital technology brings great convenience to human life, while also shows us the problems and challenges. Relying on easy-to-use image editing tools, some malicious manipulations, such as image forgery, have already threatened the authenticity of information, especially the electronic evidence in the crimes. As a result, digital forensics attracts more and more attention of researchers. Since some general post-operations, like widely used smooth filtering, can affect the reliability of forensic methods in various ways, it is also significant to detect them. Furthermore, the determination of detailed filtering parameters assists to recover the tampering history of an image. To deal with this problem, we propose a new approach based on convolutional neural networks (CNNs). Through adding a transform layer, obtained distinguishable frequency-domain features are put into a conventional CNN model, to identify the template parameters of various types of spatial smooth filtering operations, such as average, Gaussian and median filtering. Experimental results on a composite database show that putting the images directly into the conventional CNN model without transformation can not work well, and our method achieves better performance than some other applicable related methods, especially in the scenarios of small size and JPEG compression.
Similar content being viewed by others
References
Bahrami K, Kot AC (2015) Image splicing localization based on blur type inconsistency. IEEE Trans Inf Forensics Secur 10(5):999–1009
Bas P, Filler T, Pevný T (2011) Break our steganographic system: The ins and outs of organizing BOSS. In: International Conference on Information Hiding, pp 59–70
Cao G, Zhao Y, Ni R, Yu L, Tian H (2010) Forensic detection of median filtering in digital images. In: 2010 IEEE International Conference on Multimedia and expo (ICME), IEEE pp 89–94
Chen C, Ni J, Huang J (2013) Blind detection of median filtering in digital images: A difference domain based approach . IEEE Trans Image Process 22:4699–4710
Chen J, Kang X, Liu Y, Wang ZJ (2015) Median filtering forensics based on convolutional neural networks. IEEE Signal Process Lett 22:1849–1853
Chen J, Song X, Nie L, Wang X, Zhang H, Chua TS (2016) Micro tells macro: Predicting the popularity of micro-videos via a transductive model. In: ACM Multimedia Conference, pp 898–907
Christlein V, Riess C, Jordan J, Riess C, Angelopoulou E (2012) An evaluation of popular copy-move forgery detection approaches. IEEE Trans Inf Forensics Secur 7(6):1841–1854
Fukushima K (1980) Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern 36(4):193–202
Gloe T, Bohme R (2010) Dresden image database for benchmarking digital image forensics. In: Proceedings 2010 ACM symp. on appl. computing, Sierre, Switzerland, Mar. 22-26, pp 1584 –1590
Glorot X, Bordes A, Bengio Y (2010) Deep sparse rectifier neural networks. J Mach Learn Res 15
Gui X, Li X, Qi W, Yang B (2014) Blind median filtering detection based on histogram features. In: Asia-pacific signal and information processing Association, 2014 Annual Summit and Conference (APSIPA), IEEE, pp 1–4
Heygster G (1982) Rank filters in digital image processing. Comput Graphics Image Process 19:148–164
Hubel DH, Wiesel TN (1962) Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J Physiol 160(1):106–154
Justusson B (1981) Median filtering: Statistical properties. Springer
Kang X, Stamm MC, Peng A, Liu KJR (2013) Robust median filtering forensics using an autoregressive model. IEEE Trans Inf Forensics Secur 8:1456–1468
Karayev S, Trentacoste M, Han H, Agarwala A, Darrell T, Hertzmann A, Winnemoeller H (2013) Recognizing image style. Computer Science
Kirchner M, Bohme R (2008) Hiding traces of resampling in digital images. IEEE Trans Inf Forensics Secur 3:582–592
Kirchner M, Fridrich J (2010) On detection of median filtering in digital images. In: Proceedings of SPIE - The International Society for Optical Engineering 7541:1–12
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Proces Syst 25(2):2012
Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324
Liu X, Song M, Tao D, Liu Z, Zhang L, Chen C, Bu J (2013) Semi-supervised node splitting for random forest construction. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 492–499
Liu Z, Chiew K, He Q, Huang H, Huang B (2014) Prior-free rare category detection: More effective and efficient solutions. Expert Syst Appl Inter J 41(17):7691–7706
Liu Z, Chiew K, Zhang L, Zhang B, He Q, Zimmermann R (2016) Rare category exploration via wavelet analysis: Theory and applications. Expert Syst Appl 63:173–186
Luo W, Huang J, Qiu G (2010) JPEG error analysis and its applications to digital image forensics . IEEE Trans Inf Forensics Secur 5:480–491
Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines vinod nair. In: International Conference on Machine Learning, pp 807–814
Salakhutdinov R, Hinton G (2009) Deep boltzmann machines. J Mach Learn Res 5(2):1967–2006
Schaefer G, Stich M (2003) UCID: an uncompressed color image database. In: Electronic Imaging 2004, International Society for Optics and Photonics, pp 472–480
Scherer D, Müller A, Behnke S (2010) Evaluation of pooling operations in convolutional architectures for object recognition. In: Artificial Neural Networks - ICANN 2010 - International Conference, Thessaloniki, Greece, September 15-18, 2010, Proceedings, pp 92–101
Song X, Ming ZY, Nie L, Zhao YL, Chua TS (2016) Volunteerism tendency prediction via harvesting multiple social networks. ACM Trans Inf Syst 34(2)
Stamm MC, Liu K (2010) Forensic detection of image manipulation using statistical intrinsic fingerprints. IEEE Trans Inf Forensics Secur 5:492–506
Stamm MC, Liu KJR (2011) Anti-forensics of digital image compression. IEEE Trans Inf Forensics Secur 6:1050–1065
Velleman PF (1980) Definition and comparison of robust nonlinear data smoothing algorithms. J Am Stat Assoc 75:609–615
Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol PA (2010) Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11(6):3371–3408
Wang W, Yan Y, Zhang L, Hong R, Sebe N (2016) Collaborative sparse coding for multiview action recognition. IEEE Multimedia Magazine 23(4):80–87
Yuan HD (2011) Blind forensics of median filtering in digital images. IEEE Trans Inf Forensics Secur 6:1335–1345
Zhang L, Han Y, Yang Y, Song M, Yan S, Tian Q (2013a) Discovering discriminative graphlets for aerial image categories recognition. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society 22(12):5071–5084
Zhang L, Song M, Liu Z, Liu X, Bu J, Chen C (2013b) Probabilistic graphlet cut: Exploiting spatial structure cue for weakly supervised image segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 1908–1915
Zhang L, Song M, Zhao Q, Liu X, Bu J, Chen C (2013c) Probabilistic graphlet transfer for photo crop**. IEEE Trans Image Process 22(2):802–15
Zhang L, Gao Y, Hong C, Feng Y, Zhu J, Cai D (2014a) Feature correlation hypergraph: exploiting high-order potentials for multimodal recognition. IEEE Trans Cybernetics 44(8):1408–1419
Zhang L, Gao Y, Ji R, **. IEEE Trans Image Process 23(5):2235–45
Zhang L, Gao Y, **a Y, Lu K, Shen J, Ji R (2014c) Representative discovery of structure cues for weakly-supervised image segmentation. IEEE Trans Multimedia 16(2):470–479
Zhang L, Yang Y, Gao Y, Yu Y, Wang C, Li X (2014d) A probabilistic associative model for segmenting weakly-supervised images. IEEE Trans Image Process 23(9):4150–4159
Zhang L, Hong R, Gao Y, Ji R, Dai Q, Li X (2015a) Image categorization by learning a propagated graphlet path. IEEE Transactions on Neural Networks and Learning Systems 27(3):674–685
Zhang L, Li X, Nie L, Yan Y, Zimmermann R (2015b) Semantic photo retargeting under noisy image labels. ACM Trans Multimed Comput Commun Appl 12 (3)
Zhang L, Wang M, Hong R, Yin BC (2015c) Large-scale aerial image categorization using a multitask topological codebook. IEEE Trans Cybernetics 46(1)
Zhang L, Li X, Nie L, Yang Y, **a Y (2016a) Weakly supervised human fixations prediction. IEEE Trans Cybernetics 46(1):258–269
Zhang L, Yang Y, Wang M, Hong R (2016b) Detecting densely distributed graph patterns for fine-grained image categorization. IEEE Trans Image Process 25 (2):553–565
Zhang N, Paluri M, Ranzato M, Darrell T, Bourdev L (2014e) PANDA: Pose aligned networks for deep attribute modeling. In: Computer Vision and Pattern Recognition, pp 1637–1644
Zhang Y, Li S, Wang S, Shi YQ (2014f) Revealing the traces of median filtering using high-order local ternary patterns. IEEE Signal Process Lett 21:275–279
Zhou B, Garcia AL, **ao J, Torralba A, Oliva A (2014) Learning deep features for scene recognition using places database. Adv Neural Inf Proces Syst 1:487–495
Author information
Authors and Affiliations
Corresponding author
Additional information
This work was supported in part by the National Natural Science Foundation of China (61572356, 61472275, 61303208), the Tian** Research Program of Application Foundation and Advanced Technology (15JCYBJC16200), a grant from the China Scholarship Council (201506255073), and a grant from the Elite Scholar Program of Tian** University (2014XRG-0046).
Rights and permissions
About this article
Cite this article
Liu, A., Zhao, Z., Zhang, C. et al. Smooth filtering identification based on convolutional neural networks. Multimed Tools Appl 78, 26851–26865 (2019). https://doi.org/10.1007/s11042-016-4251-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-016-4251-z