Deep-BIF: Blind Image Forensics Based on Deep Learning

Digital images are widely used in all aspects of human society because of their intuitiveness, such as computer forensics and scientific research. Tampering digital images in special fields maliciously, may change the information contained therein, form false information, and cause harm to society. At present, most of the mainstream image forensics algorithms are limited by their dependence on image capture devices or lack of robustness to complex images. We hope to get rid of the above limitations by virtue of the excellent feature extraction ability of deep learning. In this work, we proposed a novel method for blind image forensics analysis based on convolutional neural networks named Deep-BIF. Furthermore, we integrated the rich models for steganalysis of digital images into our network, for the purpose of guiding network training progress with several artificial prior knowledge. We tested our method on CASIA v2.0 dataset and achieved 0.976 in terms of accuracy.

[1]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[2]  C. Lawrence Zitnick,et al.  Edge Boxes: Locating Object Proposals from Edges , 2014, ECCV.

[3]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .

[4]  Hong Zhao,et al.  Markovian Rake Transform for Digital Image Tampering Detection , 2011, Trans. Data Hiding Multim. Secur..

[5]  Umberto Ferraro Petrillo,et al.  Experimental Evaluation of an Algorithm for the Detection of Tampered JPEG Images , 2014, ICT-EurAsia.

[6]  Boquan Li,et al.  Restoration as a Defense Against Adversarial Perturbations for Spam Image Detection , 2019, ICANN.

[7]  Jing Dong,et al.  Effective image splicing detection based on image chroma , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[8]  Jing Dong,et al.  Deep learning for steganalysis via convolutional neural networks , 2015, Electronic Imaging.

[9]  Yun Q. Shi,et al.  A natural image model approach to splicing detection , 2007, MM&Sec.

[10]  Chao Liu,et al.  AdvRefactor: A Resampling-Based Defense Against Adversarial Attacks , 2018, PCM.

[11]  Jing Dong,et al.  CASIA Image Tampering Detection Evaluation Database , 2013, 2013 IEEE China Summit and International Conference on Signal and Information Processing.

[12]  Alin C. Popescu,et al.  Exposing digital forgeries in color filter array interpolated images , 2005, IEEE Transactions on Signal Processing.

[13]  Shih-Fu Chang,et al.  A model for image splicing , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[14]  Z. Jane Wang,et al.  Median Filtering Forensics Based on Convolutional Neural Networks , 2015, IEEE Signal Processing Letters.

[15]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[16]  Jing Dong,et al.  A Survey of Passive Image Tampering Detection , 2009, IWDW.

[17]  Jessica J. Fridrich,et al.  Rich Models for Steganalysis of Digital Images , 2012, IEEE Transactions on Information Forensics and Security.

[18]  Shih-Fu Chang,et al.  Blind detection of photomontage using higher order statistics , 2004, 2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512).

[19]  Hany Farid,et al.  Exposing digital forgeries through chromatic aberration , 2006, MM&Sec '06.

[20]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[21]  Belhassen Bayar,et al.  A Deep Learning Approach to Universal Image Manipulation Detection Using a New Convolutional Layer , 2016, IH&MMSec.

[22]  C. Lawrence Zitnick,et al.  Structured Forests for Fast Edge Detection , 2013, 2013 IEEE International Conference on Computer Vision.

[23]  Wei Lu,et al.  Digital image splicing detection based on Markov features in DCT and DWT domain , 2012, Pattern Recognit..