Detecting image splicing in the wild (Web) Markos Zampoglou, Symeon Papadopoulos, Yiannis Kompatsiaris 1 Centre for Research and Technology Hellas (CERTH) – Information Technologies Institute (ITI) WeMuV2015 workshop, ICME, June 29, 2015, Turin, Italy
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Detecting image splicing in the wild (Web)Markos Zampoglou, Symeon Papadopoulos, Yiannis Kompatsiaris
1Centre for Research and Technology Hellas (CERTH) – Information Technologies Institute (ITI)
WeMuV2015 workshop, ICME, June 29, 2015, Turin, Italy
A new journalistic paradigm
#2
…and its pitfalls
Blind image splicing detection
• Assume the splice differs in some aspect from the rest of the image
3. Keep the best-fitting result (bias towards success)
• For non-spliced images (true negative/false positive detection), apply the same methodology and declare a success for a blank binary map
– Main disadvantage: binary outcome, no parameters to tweak for ROC curve generation.
Evaluations
#20
• Evaluated seven algorithms:
– Double JPEG quantization (Lin et al, 2009), (Bianchi et al, 2011), (Bianchi et al, 2012a)
– Non-Aligned double JPEG quantization (Bianchi et al, 2012b)
– CFA artifacts (Ferrara et al, 2007)
– High-frequency DW noise (Mahdian et al, 2009)
– JPEG ghosts (Farid, 2010)
• Comparing median values:
Evaluation results: Emulated datasets (1/2)
#21
Dataset(Lin et al,
2009)
(Bianchi et
al, 2011)
(Ferrara et
al, 2007)
(Bianchi
et al,
2012b)
(Bianchi
et al,
2012b)
(Mahdian
et al,
2009)
Columbia
Uncomp.
Orig.
JPEG
Resized
- -
0.89 (0.05)
0.05 (0.05)
0.03 (0.04)
- -
0.39 (0.04)
0.09 (0.05)
0.11 (0.05)
VIPP
Synthetic
Orig.
JPEG
Resized
0.47 (0.05)
0.30 (0.04)
0.05 (0.05)
0.51 (0.05)
0.43 (0.04)
0.05 (0.05)
0.15 (0.05)
0.16 (0.05)
0.05 (0.04)
0.57 (0.01)
0.39 (0.05)
0.05 (0.05)
0.28 (0.05)
0.16 (0.05)
0.05 (0.05)
0.13 (0.05)
0.10 (0.05)
0.06 (0.05)
VIPP
Realistic
Orig.
JPEG
Resized
0.54 (0.04)
0.32 (0.04)
0.13 (0.04)
0.58 (0.04)
0.36 (0.04)
0.12(0.06)
0.04 (0.04)
0.04 (0.04)
0.03 (0.04)
0.70 (0.04)
0.51 (0.04)
0.23 (0.04)
0.28 (0.04)
0.17 (0.04)
0.17 (0.04)
0.20 (0.04)
0.20 (0.04)
0.18 (0.04)
• Proposed evaluation framework:
Evaluation results: Emulated datasets (2/2)
#22
Dataset(Lin et al,
2009)
(Bianchi et
al, 2011)
(Ferrara et
al, 2007)
(Bianchi
et al,
2012b)
(Bianchi
et al,
2012b)
(Mahdian
et al,
2009)
Columbia
Uncomp.
Orig.
JPEG
Resized
- -
0.66 (0.16)
0.00 (0.20)
0.00 (0.24)
- -
0.12 (0.57)
0.02 (0.86)
0.04 (0.79)
VIPP
Synthetic
Orig.
JPEG
Resized
0.44 (0.27)
0.26 (0.30)
0.00 (0.23)
0.52 (0.00)
0.30 (0.10)
0.00 (0.00)
0.01 (0.23)
0.01 (0.28)
0.00 (0.23)
0.58 (0.09)
0.23 (0.27)
0.00 (0.15)
0.04 (0.25)
0.01 (0.29)
0.00 (0.29)
0.04 (0.74)
0.04 (0.74)
0.00 (0.84)
VIPP
Realistic
Orig.
JPEG
Resized
0.41 (0.46)
0.13 (0.44)
0.00 (0.47)
0.38 (0.09)
0.17 (0.29)
0.00 (0.00)
0.09 (0.22)
0.00 (0.25)
0.00 (0.28)
0.23 (0.30)
0.14 (0.46)
0.03 (0.25)
0.03 (0.39)
0.01 (0.43)
0.01 (0.47)
0.04 (0.90)
0.02 (0.90)
0.01 (0.47)
Evaluation results: Emulated datasets (4/4)
#23
• Methods behave generally as expected
– CFA patterns destroyed by the first JPEG compression
• (Mahdian et al, 2009) is not particularly effective, but shows little vulnerability to alterations
• DQ methods show some degree of robustness to recompression only
• Rescaling is extremely disruptive, as expected
Evaluation results: Wild Web dataset (1/2)
#24
• 36 out of 82 cases were successfully detected by at least one method
– Not a single image gave good results for the other 46 cases, for any algorithm
(Lin et
al, 2009)
(Bianchi et
al, 2011)
(Ferrara et
al, 2007)
(Bianchi et
al, 2012b)
(Bianchi et
al, 2012b)
(Mahdian
et al, 2009)
(Farid,
2010)
Detections 13 12 1 8 5 15 29
Unique 4 1 0 1 2 6 10
Evaluation results: Wild Web dataset (2/2)
#25
• The noise-based method of (Mahdian et al, 2009) proved disproportionately successful,– We should not forget how prone to false positives it is.
• JPEG Ghosts are very robust, if we can manage the amount of output they produce
• Even in the cases where successful detection occurred, only a few images were correctly detected– 1386 images in the entire dataset (~ 14.3%)
– Excluding the three easiest classes, only 333 out of 8580 images were detected (~ 3.9%)
Forgery detection in the Wild (1/4)
#26
Forgery detection in the Wild (2/4)
#27
Forgery detection in the Wild (3/4)
#28
Forgery detection in the Wild (4/4)
#29
Conclusions
• In the web, very few images retain traces which are detectable with today’s state-of-the-art forensic approaches
• It is difficult to estimate the relative age of each instance of a viral image
• DQ-based methods give results with the highest confidence, but are not particularly robust
• JPEG Ghosts demonstrate significantly higher robustness than other methods, but produce large amounts of noisy output
• DW high-frequency noise also appears to give good results, but seems extremely prone to false positives
#30
Future steps
• For the web journalism case, robustness ought to be a central consideration for future algorithm evaluations
• The Wild Web dataset is freely distributed for research purposes– Due to copyright considerations, this is currently only feasible through direct contact– The dataset should be maintained to incorporate new cases of forgeries, as they
come out
• Advance the state-of-the-art by focusing on more robust traces of splicing
• Following the life-cycle of images on the web can help locate their earliest versions and build an account of the alterations that have taken place (Kennedy & Chang, 2008)
• The question remains: to what extent is the task feasible? When can we be certain that all traces have been lost?
#31
References
#32
• Bianchi, Tiziano, Alessia De Rosa, and Alessandro Piva. "Improved DCT coefficient analysis for forgery localization in JPEG images." In Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on, pp. 2444-2447. IEEE, 2011.
• Bianchi, Tiziano and Alessandro Piva, “Image forgery localization via block-grained analysis of JPEG artifacts,” IEEE Transactions on Information Forensics and Security, vol. 7, no. 3, pp. 1003–1017, 2012.
• Ferrara, Pasquale, Tiziano Bianchi, Alessia De Rosa, and Alessandro Piva. "Image forgery localization via fine-grained analysis of cfa artifacts." Information Forensics and Security, IEEE Transactions on 7, no. 5 (2012): 1566-1577.
• Farid, Hany. "Exposing digital forgeries from JPEG ghosts." Information Forensics and Security, IEEE Transactions on 4, no. 1 (2009): 154-160.
• Fontani, Marco, Tiziano Bianchi, Alessia De Rosa, Alessandro Piva, and Mauro Barni. "A framework for decision fusion in image forensics based on dempster–shafer theory of evidence." Information Forensics and Security, IEEE Transactions on 8, no. 4 (2013): 593-607.
• Kennedy, Lyndon, and Shih-Fu Chang. "Internet image archaeology: automatically tracing the manipulation history of photographs on the web." In Proceedings of the 16th ACM international conference on Multimedia, pp. 349-358. ACM, 2008.
• Lin, Zhouchen, Junfeng He, Xiaoou Tang, and Chi-Keung Tang. "Fast, automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis." Pattern Recognition 42, no. 11 (2009): 2492-2501.
• Mahdian, Babak and Stanislav Saic, “Using noise inconsistencies for blind image forensics,” Image and Vision Computing, vol. 27, no. 10, pp. 1497–1503, 2009.