Automated Image Forgery Detection through Classification of …€¦ · Aug. 30th, 2012 | Christian...

Automated Image Forgery Detection

through Classification of JPEG Ghosts

Dipl.-Inf. Christian Riess (joint work with Fabian Zach and Elli Angelopoulou)

August 30th, 2012

Pattern Recognition Lab (CS 5)

University of Erlangen-Nuremberg

Aug. 30th, 2012 | Christian Riess | Pattern Recognition Lab (CS 5) | Forgery Detection through Classification of JPEG Ghosts

Image forensics

● Is a picture authentic?

● Has a picture been taken with a

particular camera?

● Emerging application in

information security, signal

processing and computer vision

Image forensics

particular camera?

Image forensics

particular camera?

Image forensics

particular camera?

Approaches to the detection of digital forgeries

Camera

ImageLens Sensor Processing

Camera

ImageLens Sensor

Geometry,

lighting

environment

Processing

Camera

ImageLens Sensor

Geometry,

lighting

environment

Chromatic

aberration

Processing

Camera

ImageLens Sensor

Geometry,

lighting

environment

Chromatic

aberration

Sensor

Processing

Camera

ImageLens Sensor

Geometry,

lighting

environment

Chromatic

aberration

Sensor

Demosaicking,

camera

response

Processing

Camera

ImageLens Sensor

Geometry,

lighting

environment

Chromatic

aberration

Sensor

Demosaicking,

camera

response

Copy-move,

resampling,

double

compression

Processing

Camera

ImageLens Sensor

Geometry,

lighting

environment

Chromatic

aberration

Sensor

Demosaicking,

camera

response

Copy-move,

resampling,

double

compression

Processing

Topic of this talk

JPEG compression

Quality 100 Quality 20

● JPEG compression is block-based and lossy

● JPEG block grid: 8 by 8 pixels

● If recompressed, the new grid can be aligned/misaligned to previous grid

JPEG compression

Quality 100 Quality 20

● JPEG compression is block-based and lossy

● JPEG block grid: 8 by 8 pixels

● If recompressed, the new grid can be aligned/misaligned to previous grid

● This work: Classification of single- vs double-compressed JPEG blocks

● JPEG is very popular “bad men” use JPEG, too

● If an image is recompressed, the statistics of JPEG artifacts change

● Forensic scenario:

● First JPEG compression: in camera

● Second JPEG compression: e.g. in a postprocessing tool

● If only part of an image is double-compressed? Maybe a manipulation!

JPEG artifacts as manipulation cues

● JPEG is very popular “bad men” use JPEG, too

● If an image is recompressed, the statistics of JPEG artifacts change

● Forensic scenario:

● First JPEG compression: in camera

● Second JPEG compression: e.g. in a postprocessing tool

● If only part of an image is double-compressed? Maybe a manipulation!

JPEG artifacts as manipulation cues

Forensic question: “Are the JPEG artifacts consistent?”

Manipulation scenario: image splicing

Background, JPEG

compressed

Foreground, JPEG

compressed

Forgery, JPEG compressed

Manipulation scenario: image splicing

Background, JPEG

compressed

Foreground, JPEG

compressed

Forgery, JPEG compressed

Background:

double-compressed

Cat (foreground):

single-compressed(due to editing)

Foreground is adjusted to

background

(e.g., by cutting, cropping,

painting, smearing)

this destroys original (primary)

JPEG artifacts

● Common approach:

● Inspect coefficients of the discrete cosine transform

(e.g. Lukas et al. 2003, He et al. 2005, Ye et al. 2007, Huang et al. 2010)

● Tricky: case-by-case analysis, depending on compression parameters

● e.g. primary (first) compression == secondary compression,

● or secondary quantization factors multiples of primary factors

Detecting forgeries from JPEG inconsistencies

● Common approach:

● Inspect coefficients of the discrete cosine transform

(e.g. Lukas et al. 2003, He et al. 2005, Ye et al. 2007, Huang et al. 2010)

● Tricky: case-by-case analysis, depending on compression parameters

● e.g. primary (first) compression == secondary compression,

● or secondary quantization factors multiples of primary factors

Detecting forgeries from JPEG inconsistencies

JPEG Ghosts [1] exploit a different cue

• Applicable if primary compression < secondary compression

• Simple to explain and implement

• But: manual browsing of 100’s of images required

[1] Hany Farid, “Exposing Digital Forgeries from JPEG Ghosts” in IEEE Transactions on Information Forensics and Security, vol. 1, no. 4,

2009, pp. 154-160.

● Read JPEG compression parameters from image header

● Recompress an image with lower qualities , ,…

● Look at difference images , ,…

● …and the “ghost” appears

The JPEG Ghost observation

(synthetic) double-

compressed square

(synthetic) double-

compressed square

(synthetic) double-

compressed square

Finding Ghosts requires patience

Ghost hardly

visible if

Ghost hardly

visible if

block grid is not aligned

Ghost hardly

visible if

block grid is not aligned[try all 64

alignments!]

first and second

compression parameters

of similar magnitude

Ghost hardly

visible if

alignments!]

first and second

Ghost hardly

visible if

alignments!]

sharper!]

first and second

Ghost hardly

visible if

alignments!]

sharper!]

up to 500 low-

contrast b/w

images.

first and second

Ghost hardly

visible if

alignments!]

sharper!]

up to 500 low-

contrast b/w

images.

Contribution of this work:

Complete Automation of the JPEG Ghost Scheme

● For each JPEG block

● track , ,… over recompression steps , ,…

● extract features,

● classify the block.

Extraction of Ghost data

JPEG recompression quality

rence to input im

Feature extraction Single compression Double compression

● Weighted mean

● Median

● Slope of regression line

● Y-axis intercept of

regression line

● Weighted sum of points

below 0.5

● SSD between diagonal

and curve

rence to input im

JPEG recompression quality JPEG recompression quality

Feature histograms: double vs. single-compressed

Median

Weighted mean Y-intercept of

regression line

Slope of

regression line

SSD below diagonal

% of points below

Classification and evaluation

[2] G. Schaefer and M. Stich, “UCID – An Uncompressed Colour Image Database,” in SPIE Storage and Retrieval Methods and Applications for

Multimedia, Jan. 2004, pp. 472-480.

● Classifiers:

● Naïve Bayes

● Multilayer Perceptron

● AdaBoost

● Random Forests

● Ghost Embeddings in UCID dataset [2]

● 1338 images, 512x384 pixels

● Three compression variants:

a) Purely single-compressed

b) 192x192 pixels double-compressed, remainder single-compressed

c) The opposite of b)

● Specificity/Sensitivity on 8x8, 16x16, 32x32 and 64x64 windows

● Metric: Specificity / Sensitivity

● Results by quality difference between first and second compression

● Best performance on 8x8 pixels ( 1 JPEG block):

● = 5: Specificity 0.82, Sensitivity 0.86

● Comparison to 8x8 pixel block method by Lin et al.:

Quantitative results (aligned grid, per image)

Qualitative results (1)

Qualitative results (2)

Summary

● Full automation of the JPEG Ghost scheme for distinguishing

single- and double-JPEG compression

● Features:

● Recompress image block with various (lower) quality levels

● Differences to input image block serve as basis for feature extraction

● 6 “straightforward-to-compute” features such as

● median of differences

● y-axis intercept of regression line

● Competitive detection rates with the simplicity of the Ghost scheme

e.g. AdaBoost:

specificity = 0.82, sensitivity = 0.86 at quality difference=5, 8x8 pixels

Automated Image Forgery Detection through Classification of …€¦ · Aug. 30th, 2012 | Christian...

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