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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?