Seminar: Image Tampering MC919 - Prof. Anderson Rocha Arthur Espíndola Ribeiro120761 Vinicius Dias de Oliveira Gardelli084197 05/11/2014.

Slides:



Advertisements
Similar presentations
Object Specific Compressed Sensing by minimizing a weighted L2-norm A. Mahalanobis.
Advertisements

Evaluating Color Descriptors for Object and Scene Recognition Koen E.A. van de Sande, Student Member, IEEE, Theo Gevers, Member, IEEE, and Cees G.M. Snoek,
PANKAJ MALVIYA RUCHIRA NASKAR
1 Image Authentication by Detecting Traces of Demosaicing June 23, 2008 Andrew C. Gallagher 1,2 Tsuhan Chen 1 Carnegie Mellon University 1 Eastman Kodak.
1 A robust detection algorithm for copy- move forgery in digital images Source: Forensic Science International, Volume 214, Issues 1–3, 10 January 2012.
Detect Digital Image Forgeries Ting-Wei Hsu. History of photo manipulation 1860 the portrait of Lincoln is a composite of Lincoln ’ s head and John Calhoun.
Ales Zita. Publication Digital Image Forgery Detection Based on Lens and Sensor Aberration Authors : Ido Yerushalmy, Hagit Hel-Or Dept. of Computer Science,
Digital Image Forensics
Color spaces CIE - RGB space. HSV - space. CIE - XYZ space.
A Comprehensive Study on Third Order Statistical Features for Image Splicing Detection Xudong Zhao, Shilin Wang, Shenghong Li and Jianhua Li Shanghai Jiao.
1 Exposing Digital Forgeries in Color Array Interpolated Images Presented by: Ariel Hutterer Final Fantasy,2001My eye.
School of Computing Science Simon Fraser University
A new approach for modeling and rendering existing architectural scenes from a sparse set of still photographs Combines both geometry-based and image.
Detecting Digital Image Forgeries Using Sensor Pattern Noise presented by: Lior Paz Jan Lukas, jessica Fridrich and Miroslav Goljan.
Recovering Intrinsic Images from a Single Image 28/12/05 Dagan Aviv Shadows Removal Seminar.
Processing Digital Images. Filtering Analysis –Recognition Transmission.
Distinguishing Photographic Images and Photorealistic Computer Graphics Using Visual Vocabulary on Local Image Edges Rong Zhang,Rand-Ding Wang, and Tian-Tsong.
Detecting Image Region Duplication Using SIFT Features March 16, ICASSP 2010 Dallas, TX Xunyu Pan and Siwei Lyu Computer Science Department University.
Real-Time Geometric and Color Calibration for Multi-Projector Displays Christopher Larson, Aditi Majumder Large-Area High Resolution Displays Motivation.
Multiple Human Objects Tracking in Crowded Scenes Yao-Te Tsai, Huang-Chia Shih, and Chung-Lin Huang Dept. of EE, NTHU International Conference on Pattern.
Image Forgery Detection by Gamma Correction Differences.
CS292 Computational Vision and Language Visual Features - Colour and Texture.
Multiple View Geometry Marc Pollefeys University of North Carolina at Chapel Hill Modified by Philippos Mordohai.
Shadow Detection In Video Submitted by: Hisham Abu saleh.
History of Digital Camera By : Dontanisha Williams P2.
Median Filtering Detection Using Edge Based Prediction Matrix The 10th IWDW, Atlantic City, New Jersey, USA 23~26 October 2011 School of Information Science.
CS Spring 2012 CS 414 – Multimedia Systems Design Lecture 8 – JPEG Compression (Part 3) Klara Nahrstedt Spring 2012.
Retaliating Anti-forensics of JPEG Image Compression Based On the Noise Level Estimation PROPOSAL SPRING 2015 ADVISOR: Dr. K.R.Rao Presented by, Komandla.
Introduction to Multimedia Security Topics Covered in this Course Multimedia Security.
Lab #5-6 Follow-Up: More Python; Images Images ● A signal (e.g. sound, temperature infrared sensor reading) is a single (one- dimensional) quantity that.
How A Camera Works Image Sensor Shutter Mirror Lens.
Klara Nahrstedt Spring 2011
Introduction to Visible Watermarking IPR Course: TA Lecture 2002/12/18 NTU CSIE R105.
Digital Face Replacement in Photographs CSC2530F Project Presentation By: Shahzad Malik January 28, 2003.
CSCE 5013 Computer Vision Fall 2011 Prof. John Gauch
Indiana University Purdue University Fort Wayne Hongli Luo
Forgery & Forensics Hany Farid ACM Proceedings of the 8th Workshop on Multimedia and Security, Sep
Retaliating Anti-forensics of JPEG Image Compression Based On the Noise Level Estimation FINAL PRESENTATION SPRING 2015 ADVISOR: Dr. K.R.Rao Presented.
Goal and Motivation To study our (in)ability to detect inconsistencies in the illumination of objects in images Invited Talk! – Hany Farid: Photo Forensincs:
Computer Vision Lecture #10 Hossam Abdelmunim 1 & Aly A. Farag 2 1 Computer & Systems Engineering Department, Ain Shams University, Cairo, Egypt 2 Electerical.
Advances in digital image compression techniques Guojun Lu, Computer Communications, Vol. 16, No. 4, Apr, 1993, pp
Exposing Digital Forgeries in Color Filter Array Interpolated Images By Alin C. Popescu and Hany Farid Presenting - Anat Kaspi.
Introduction to Digital Signals
A NOVEL METHOD FOR COLOR FACE RECOGNITION USING KNN CLASSIFIER
Color and Brightness Constancy Jim Rehg CS 4495/7495 Computer Vision Lecture 25 & 26 Wed Oct 18, 2002.
Automated Fingertip Detection
Project Topic : Image Differentiation Name : Bo Li Supervisor: Dr. Jimmy Li.
WCRP Extremes Workshop Sept 2010 Detecting human influence on extreme daily temperature at regional scales Photo: F. Zwiers (Long-tailed Jaeger)
STATISTIC & INFORMATION THEORY (CSNB134) MODULE 11 COMPRESSION.
1Ellen L. Walker 3D Vision Why? The world is 3D Not all useful information is readily available in 2D Why so hard? “Inverse problem”: one image = many.
DIGITAL VIDEO AUTHENTICATION. Contents  What is Quantization ?  What is Double MPEG/JPEG Compression?  Video Compression/Decompression  What is JPEG/Frame.
 Forensics of image re-sampling (such as image resizing) is an important issue,which can be used for tampering detection, steganography, etc.  Most of.
Copy-Paste Forgery Detection Exposing Digital Forgeries by Detecting Duplicated Image Regions (A. Popescu and H. Farid) Detection of Copy-Move Forgery.
Digital Image Forensics CS 365 By:- - Abhijit Sarang - Pankaj Jindal.
1 Marco Carli VPQM /01/2007 ON BETWEEN-COEFFICIENT CONTRAST MASKING OF DCT BASIS FUNCTIONS Nikolay Ponomarenko (*), Flavia Silvestri(**), Karen.
WCPM 1 Chang-Tsun Li Department of Computer Science University of Warwick UK Image Clustering Based on Camera Fingerprints.
IMAGE FORGERY DETECTION Submitted by Deepika Dileep Deepika Dileep S7 IT N0:35 N0:35.
Learning and Removing Cast Shadows through a Multidistribution Approach Nicolas Martel-Brisson, Andre Zaccarin IEEE TRANSACTIONS ON PATTERN ANALYSIS AND.
PERFORMANCE ANALYSIS OF VISUALLY LOSSLESS IMAGE COMPRESSION
DCT IMAGE COMPRESSION.
Compression for Synthetic Aperture Sonar Signals
CS4670 / 5670: Computer Vision Kavita Bala Lec 27: Stereo.
Exposing Digital Forgeries Through Chromatic Aberration Micah K
Image Forgery JPEG Compression Based Forgery Detection
Tone Dependent Color Error Diffusion
Exposing Digital Forgeries by Detecting Traces of Resampling Alin C
IMAGE FORGERY DETECTION
Introduction to Multimedia Security Topics Covered in this Course
Presentation transcript:

Seminar: Image Tampering MC919 - Prof. Anderson Rocha Arthur Espíndola Ribeiro Vinicius Dias de Oliveira Gardelli /11/2014

2014 Seminar Series - Digital Forensics (MO447/MC919) Outline [1/2] 1.Introduction 1. What is Image Tampering? 2. History 1.Tampering detection 1. Pixel-based techniques 2. Format-based techniques 3. Camera-based techniques 4. Physics-based techniques 5. Geometry-based techniques

2014 Seminar Series - Digital Forensics (MO447/MC919) Outline [2/2] 3.Selected Techniques 1. JPEG Ghosts Detection 2. Inconsistencies in Shadows 3.Conclusion References

2014 Seminar Series - Digital Forensics (MO447/MC919) Introduction

2014 Seminar Series - Digital Forensics (MO447/MC919) What is Image Tampering? From the dictionary: ➢ Tamper: Interfere with (something) in order to cause damage or make unauthorized alterations

2014 Seminar Series - Digital Forensics (MO447/MC919) What is Image Tampering? From Wikipédia: ➢ Image manipulation: It’s the application of image editing techniques to photographs in order to create an illusion or deception after the original photographing took place

2014 Seminar Series - Digital Forensics (MO447/MC919) History ●As old as photography itself

2014 Seminar Series - Digital Forensics (MO447/MC919) ~1860: Iconic Abraham Lincoln Photo was in fact a composition of his head and John Calhoun’s body. [Fourandsix/Hist]

2014 Seminar Series - Digital Forensics (MO447/MC919) History ●As old as photography itself ●Has been extensively used for political and artistic reasons

2014 Seminar Series - Digital Forensics (MO447/MC919) ~1930: Stalin had a commissar removed from the original photograph after the man fell out of favor with him. [Fourandsix/Hist]

2014 Seminar Series - Digital Forensics (MO447/MC919) 1970: Pullitzer prize winning photo had a pole removed from behind the screaming woman. [Fourandsix/Hist]

2014 Seminar Series - Digital Forensics (MO447/MC919) 1989: Oprah’s face was spliced onto actress Ann-Margaret’s body for the cover of a magazine. Neither women had agreed upon the montage beforehand. [Fourandsix/Hist]

2014 Seminar Series - Digital Forensics (MO447/MC919) History ●As old as photography itself ●Has been extensively used for political and artistic reasons ●More easily achieved over time

2014 Seminar Series - Digital Forensics (MO447/MC919) 2014: Recent forgery of a vote count report in an attempt to invalidate Brazilian presidential elections. [eFarsas/Dilma]

2014 Seminar Series - Digital Forensics (MO447/MC919) Tampering Detection

2014 Seminar Series - Digital Forensics (MO447/MC919) ●Techniques can be separated in roughly 5 categories [Farid 2009a]: o Pixel-based techniques o Format-based techniques o Camera-based techniques o Physics based techniques o Geometry-based techniques Tampering detection

2014 Seminar Series - Digital Forensics (MO447/MC919) Pixel-based techniques ●Pixels are the building blocks of images ●Image manipulation disrupts statistical properties of the pixels ●Directly or indirectly analyzes pixel-level correlations that arise from a specific form of tampering

2014 Seminar Series - Digital Forensics (MO447/MC919) Pixel-based techniques ●Cloning Cloned regions can be of any shape and location. ●Resampling Introduces specific periodic correlations between neighbouring pixels. ●Splicing Disrupts higher-order Fourier Statistics. ●Statistical Photographs contain specific statistical properties.

2014 Seminar Series - Digital Forensics (MO447/MC919) Format-based techniques ●Lossy compression introduces artifacts ●Rely on image compression specificities to detect forgery ●JPEG is the most common format

2014 Seminar Series - Digital Forensics (MO447/MC919) Format-based techniques ● Focus on three techniques o Double JPEG compression detection [Lukas & Fridrich 2003]

2014 Seminar Series - Digital Forensics (MO447/MC919) Histograms of four image quantizations. Double compression introduces periodic artifacts in the image histogram. [Farid 2009a]

2014 Seminar Series - Digital Forensics (MO447/MC919) Format-based techniques ● Focus on three techniques o Double JPEG compression detection [Lukas & Fridrich 2003] o JPEG Blocking artifacts [Luo et al. 2007]

2014 Seminar Series - Digital Forensics (MO447/MC919) Flower before and after heavy jpeg compression and resizing. [Wikipedia/CompArt]

2014 Seminar Series - Digital Forensics (MO447/MC919) Format-based techniques ● Focus on three techniques o Double JPEG compression detection [Lukas & Fridrich 2003] o JPEG Blocking artifacts [Luo et al. 2007] o JPEG Ghosts detection [Farid 2009b]

2014 Seminar Series - Digital Forensics (MO447/MC919) Camera-based techniques ●Cameras leave traces on generated images ●Chromatic aberration [Johnson & Farid 2006] o Variations in chromatic aberration patterns across an image may be used as evidence of image tampering

2014 Seminar Series - Digital Forensics (MO447/MC919) Color aberration generated on an image due to a camera lens’ color displacement. [Farid 2009a]

2014 Seminar Series - Digital Forensics (MO447/MC919) Camera-based techniques ●Cameras leave traces on generated images ●Chromatic aberration [Johnson & Farid 2006] ●Sensor noise o Distortions of sensor noise pattern

2014 Seminar Series - Digital Forensics (MO447/MC919) Camera-based techniques ●Cameras leave traces on generated images ●Chromatic aberration [Johnson & Farid 2006] ●Sensor noise ●Color-filter arrays o Color calculation from neighbour pixels introduces recognizable correlation patterns between pixels in an image

2014 Seminar Series - Digital Forensics (MO447/MC919) Color pattern created by a Bayer filter arrangement. [Wikipédia/Bayer]

2014 Seminar Series - Digital Forensics (MO447/MC919) Physics-based techniques ●2-D Lighting Considers only the two-dimensional (2-D) surface normals at the occluding object boundary. ●3-D Lighting Uses the model of the human eye, to determine the required 3-D surface normals. ●Light Enviroment Uses an aproximation of a Lambertian surface, simplified further to consider only the occluding boundary of an object.

2014 Seminar Series - Digital Forensics (MO447/MC919) Multiple lighting conditions for a single face. [Farid 2009a]

2014 Seminar Series - Digital Forensics (MO447/MC919) Geometric-based techniques ●Principal point estimation Principal point is the projection of the camera center onto the image plane. When a person or object is translated in the image, the principal point is moved proportionally. ●Metric measurements Tools from projective geometry that allow for the rectification of planar surfaces and, under certain conditions, the ability to make real-world measurements from a planar surface.

2014 Seminar Series - Digital Forensics (MO447/MC919) The result of planar rectification followed by histogram equalization. [Farid 2009a]

2014 Seminar Series - Digital Forensics (MO447/MC919) Selected Techniques

2014 Seminar Series - Digital Forensics (MO447/MC919) JPEG Ghosts Detection ●Explores double JPEG compression artifacts ●Detects lower-quality image patches spliced into higher quality images ●We need to understand JPEG compression

2014 Seminar Series - Digital Forensics (MO447/MC919) JPEG Compression Scheme ●Converts image from RGB to YCbCr color space ●Chroma channel subsampling o Usually 4:2:0 o Human eye less responsive to chromatic variations than to luminance variations ●Breaks Images in 8x8 pixel blocks

2014 Seminar Series - Digital Forensics (MO447/MC919) JPEG Compression Scheme ●Calculates 8x8 DCT coefficient matrices for each block: ●Quantizes the 8x8 DCT coefficient matrices using a quantizing matrix K for each channel:

2014 Seminar Series - Digital Forensics (MO447/MC919) DCT basis functions for a 8x8 image. [Wikipédia/JPEG]

2014 Seminar Series - Digital Forensics (MO447/MC919) JPEG Ghosts Detection ●Difference between initial compression and second compression is minimal when quality rate is the same ●Differences are calculated directly from pixel color values

2014 Seminar Series - Digital Forensics (MO447/MC919) Squared difference between coefficients originally quantized with factor q 0 =17 followed by quantization q 1 ∈ [1,30]. [Farid 2009b]

2014 Seminar Series - Digital Forensics (MO447/MC919) Squared difference between coefficients originally quantized with factor q 0 =23, followed by q 1 =17 and q 2 ∈ [1,30]. [Farid 2009b]

2014 Seminar Series - Digital Forensics (MO447/MC919) ●Problem: low-frequency regions (e.g: blue sky) may have lower difference values even for different quantization factors ●Solution: average differences over a square region and normalize values to fit in the interval [0,1] JPEG Ghosts Detection

2014 Seminar Series - Digital Forensics (MO447/MC919) ●Problem: low-frequency regions (e.g: blue sky) may have lower difference values even for different quantization factors ●Solution: average differences over a square region and normalize values to fit in the interval [0,1] JPEG Ghosts Detection

2014 Seminar Series - Digital Forensics (MO447/MC919) Top Left: Original image compressed at 85% quality with center re-saved at 65% quality. Rest: Differences between original image and re-saves at many qualities. [Farid 2009b]

2014 Seminar Series - Digital Forensics (MO447/MC919) ●JPEG Ghosts are usually visibly salient ●Still useful to quantify if a given region is different from the rest of the image o Two-factor Kolmogorov-Smirnov statistic JPEG Ghosts Detection

2014 Seminar Series - Digital Forensics (MO447/MC919) JPEG Ghosts Detection Results ●Threshold selected to yield less than 1% false positive rates ●KS statistics considered for each image difference Accuracy for different image sizes and quality variations. [Farid 2009b]

2014 Seminar Series - Digital Forensics (MO447/MC919) Inconsistencies in Shadows ●Technique for determining if cast and attached shadows in a photo are consistent with the model of a single distant or local point light source. ●Analyzing lighting and shadows are attractive. ●Relaxed constraints specify either angular wedges or half- planes in the image. ●Places no assumptions on the scene geometry.

2014 Seminar Series - Digital Forensics (MO447/MC919) Examples of cast and attached shadow constraints. [Farid 2013]

2014 Seminar Series - Digital Forensics (MO447/MC919) attached shadow: cast shadow: Cast and attached shadow constraints definitions. [Farid 2013]

2014 Seminar Series - Digital Forensics (MO447/MC919) Methods ●Combine the constraints into a single sistem of m inequalities: ●Account for errors or inconsistencies by introducing a set of m slack variables s i

2014 Seminar Series - Digital Forensics (MO447/MC919) Methods ●minimize the slack variables, while satisfying all of the cast and attached shadow constraints (linear programming). ●if the light is behind the center of projection: ●Greedily find an approximately minimal set of inconsistent constraints.

2014 Seminar Series - Digital Forensics (MO447/MC919) User Interface example. [Farid 2013]

2014 Seminar Series - Digital Forensics (MO447/MC919) Results

2014 Seminar Series - Digital Forensics (MO447/MC919) Results

2014 Seminar Series - Digital Forensics (MO447/MC919) Conclusion

2014 Seminar Series - Digital Forensics (MO447/MC919) Conclusion ●Many different techniques to detect image tampering ●Many ways to tamper with images

2014 Seminar Series - Digital Forensics (MO447/MC919) Questions?

2014 Seminar Series - Digital Forensics (MO447/MC919) ➢ [Fourandsix/Hist] “Photo Tampering throughout History”, available at access on 30/10/2014Photo Tampering throughout History ➢ [eFarsas/Dilma] “Fraude em urna eletrônica dá 400 votos para Dilma”, available at farsas.com/fraude-em-urna-eletronica-da-400-votos-para-dilma.html, access on 30/10/2014http:// farsas.com/fraude-em-urna-eletronica-da-400-votos-para-dilma.html ➢ [Wikipédia/JPEG] “JPEG”, available at access on 31/10/2014http://en.wikipedia.org/wiki/JPEG ➢ [Wikipédia/CompArt] “Compression artifact”, available at access on 31/10/ ➢ [Wikipédia/Bayer] “Bayer filter”, available at access on 31/10/2014http://en.wikipedia.org/wiki/Bayer_filter ➢ [Farid 2009a] Farid, H. “A Survey of Image Forgery Detection”. IEEE Signal Processing Magazine, 26(2):16-25, References

2014 Seminar Series - Digital Forensics (MO447/MC919) ➢ [Farid 2009b] Farid, H. “Exposing Digital Forgeries from JPEG Ghosts”, in Information Forensics and Security, IEEE Transactions on (Volume:4, Issue: 1 ), 2009Information Forensics and Security, IEEE Transactions on Issue: 1 ➢ [Farid 2013] Farid, H. “Exposing Photo Manipulation with Inconsistent Shadows”, ACM Transactions on Graphics, 2013 ➢ [Luo et al. 2007] W. Luo, Z. Qu, J. Huang, and G. Qiu, “A novel method for detecting cropped and recompressed image block”, in Proc. IEEE Conf. Acoustics, Speech and Signal Processing, Honolulu, HI, 2007, pp. 217–220. ➢ [Lukas & Fridrich 2003] J. Lukas and J. Fridrich, “Estimation of primary quantization matrix in double compressed JPEG images”, in Proc. Digital Forensic Research Workshop, Cleveland, OH, Aug ➢ [He et al. 2006] J. He, Z. Lin, L. Wang, and X. Tang, “Detecting doctored JPEG images via DCT coefficient analysis”, in Proc. European Conf. Computer Vision, Graz, Austria, 2006, pp. 423– 435. References

2014 Seminar Series - Digital Forensics (MO447/MC919) ➢ [Johnson & Farid 2006] M. K. Johnson and H. Farid, “Exposing digital forgeries through chromatic aberration”, in Proc. ACM Multimedia and Security Workshop, Geneva, Switzerland, 2006, pp. 48–55 References