Image Forensics of High Dynamic Range Imaging

10th International Workshop on Digital-Forensics & Watermarking

This research is sponsored by an EPSRC/Charteris CASE Award

P. J. Bateman, A. T. S. Ho, and J. A. Briffa

Image Forensics

Uncovering facts about an image without actively injecting data

Verify the integrity of a digital image

Source Classification

Camera Identification Processing History Recovery

Forgery Detection

Anomaly Investigation

M. Chen, J. Fridrich, M. Goljan, and J. Lukás, “Image Origin and Integrity Using Sensor Noise,” IEEE Transactions on Information Security and Forensics, 3(1), pp. 74-90, March 2008

H. Farid, “Digital Image Forensics”, American Academy of Forensic Sciences, Washington, DC, 2008

Auto-bracketingCamera Merging and Registration

Tone MappingLDR version of HDR Image

HDR Pipeline

HDR

-1EV

0EV

+1EV

E. Reinhard, G. Ward, S. Pattanaik, and P. Debevec, “High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting,” Morgan Kauffman, ISBN: 978-0-12-585263-0, 2005.

HDR Imaging

High Dynamic Range Imaging

High Dynamic Range Imaging

High Dynamic Range Imaging

High Dynamic Range Imaging

Image Histogram

LDR

HDR

0

30,000

60,000

0 255

0

350,000

700,000

0 255

High Dynamic Range Imaging

HDR is Popular

Stats taken from Flickr.com (24-October-2011)

iPhone 4 Camera Useage

Can we detect HDR-Processed Images?

• An increasingly popular photography method• On-board implementations• More and more HDR images will exist amongst LDR

• EXIF metadata shows little regarding HDR processed images.

• The HDR pipeline differs from manufacturer to manufacturer• Do images contain fingerprints of specific manufacturing pipelines?

• Images can look heavily processed, but are straight off camera• This may fool existing Forensic algorithms

• A novel subject of Image Forensics

Research Motivation

Processing History Recovery Anomaly Investigation

Camera Identification

HDR Detection in Image Forensics

Auto-bracketingCamera Merging and Registration

LDR version of HDR Image Tone Mapping

HDR Pipeline

• Operates on Illuminance-Reflectance model• illuminance can be reduced

• Separate illuminance and reflectance components

I(x,y) = i(x,y) · r(x,y) D = log i(x,y) + log r(x,y)

• (High-Pass) filtering in FFT domain is applied*• Attenuate low frequencies (illuminance)• Preserve high frequencies (reflectance)

*A. V. Oppenheim, R. Schafer, and T. Stockham, “Nonlinear Filtering of Multiplied and Convolved Signals,” in Proceedings of the IEEE, 56(8), pp. 12641291, 1968.

Homomorphic Filtering

“Strong” edges contain high and low frequency data

Haloing artefacts are producedG. Qiu, J. Guan, J. Duan, M. Chen, “Tone Mapping for HDR Image using Optimization: A New Closed Form Solution,” 18th International Conference on Pattern Recognition, pp. 996-999, 2006.

The Problem with HF

Aim:To accurately classify HDR/LDR images

Device Used:Apple iPhone 4 (Native Camera App)

Method:Capture 100 real-world “landscape” images• 50 HDR• 50 LDR

Images are captured from a tripod to ensure registration processing is minimised

The Experiment

HDR Image

LDR Image

LDR HDR

Spatial Pixel Distribution

The Strategy

Read Image(extract luminance)

Canny Edge

Remove Texture

Find “Strongest”

Edge

FFTEdge Data

Classify Edge Data

Majority Voting

Output

1. Read Image and Extract Luminance

2. Canny Edge Detection

3. Threshold Y to B&W

4. Morphology: “Open”

5. Sobel Edge Detection

6. Morphology: “Open”

Remove connected edges that do not satisfy:

angle > ±10 of 90°

7. Remove Weaker Edges

Remove connected edges that do not satisfy:

angle > ±10 of 90°

7. Remove Weaker Edges

8. Plot Pixel Distribution

9. Convert to FFT Domain

Training Test

No. of images

Edge vectors (per image)

Total no. of feature vectors

90 (45HDR; 45LDR)

10 (5HDR; 5LDR)

100 100

9,000 1,000

2 classes: LDR / HDR

Essentially classifying each edge independently

10. SVM: Train and Classify

• Each classification from test set is mapped back to its respective image set (100 per image)

• Majority voting of the results to yield overall image classification

Results

• Each classification from test set is mapped back to its respective image set (100 per image)

• Majority voting of the results to yield overall image classification

Test Image Actual Predicted Confidence

1

2

3

4

5

6

7

8

9

10

LDR LDR 87

LDR LDR 92

LDR LDR 100

LDR LDR 91

LDR LDR 90

HDR HDR 88

HDR HDR 99

HDR HDR 80

HDR HDR 69

HDR HDR 55

Results

• A proof-of-concept has been presented for detecting HDR processed images

• Halo artefact present in iPhone 4 HDR edges

• Large peak in intensity values characterised at strong edge points

• Strategy for detecting strong edges identified

• Scheme tested and trained on 100 images

• Classification accuracy of 100% (after majority voting)

Summary

• Strengthen strategy for detecting strong edges

• Consider edges of all possible orientations

• Greatly increase image set size

• Extend to classify from more device sources

• Classify HDR Apps that created the image

Future Work

Questions?