1. IMAGE FORGERY DETECTION Submitted by Deepika Dileep Deepika Dileep S7 IT N0:35 N0:35

2. Contents Introduction Digital Watermarking Image Forensic Tools Conclusion

3. Introduction We are living in an age where we are exposed to remarkable array of visual imagery. Today's digital technology had begun to erode the integrity of images. Over the past few years,the field of digital forensics has emerged to restore some trust to digital images.

4. Digital Watermarking One solution to image authentication problem is digital watermarking. Water marking is actually a technique of message hiding in some image or text. The main advantage of using watermarks is to encode information which can prove ownership, e.g., copyrights. The drawback of this approach is that a watermark must be inserted at the time of recording, which would limit to specially equipped digital cameras.

5. Watermarking

6. Image Forensic Tools Over the last few years, there has been a growing body of work on tools for digital image forensics. These tools are capable of detecting tampering in images from any camera, without relying on watermarks or specialized hardware. Instead of watermarks, these tools assume that images possess certain regularities that are disturbed by tampering.

7. Tools Pixel-based Format-based Camera-based Physical-based

8. Pixel-based The emphasis is on pixel Techniques for detecting tampering:  Cloning  Resampling  Splicing  Statistical

9. Cloning The most common image manipulations is to clone portions of the image to conceal a person or object into screen. If done with care, it is difficult to detect cloning visually. Two algorithms have been developed to detect cloned image regions:  PCA (Principal Component Analysis)  DCT (Discrete Cosine Transformation)

10. Resampling To create a convincing composite, it is often necessary to resize, rotate or stretch portions of an image. Resampling introduces specific correlations between neighboring pixels. If it is known which pixels are correlated with their neighbors, then specific form of correlation is easily determined. But if neither is known, then a two step iterative algorithm EM (expectation/maximization) is used to solve the problem.

11. Splicing A common form of photographic manipulation is the digital splicing of two or more images into a single composite. If spicing is done, then it will disrupt higher-order statistics which results in tampering.

12. Statistical To randomly draw from the possible set of pixel combinations to obtain statistical regularities in natural images to detect image manipulations. Compute first and higher order statistics from a wavelet decomposition. Compute the bit agreements and disagreements across bit planes. Use linear search algorithm called Floating Forward algorithm, which differentiate authentic from manipulated images. Detect manipulations like resizing, filtering etc.

13. Format-Based Unique properties of lossy compression can be exploited for forensic analysis. Forensic technique that detect tampering deals with JPEG lossy compression scheme.

14. JPEG Quantization RGB image is first converted into luminance/chrominance space. The full quantization is achieved by DCT method which produce a table of 192 values representing the channel. These values changes according to low and high compression rates. When an image block is individually transformed or quantized,artifacts appear at the border of neighboring blocks.

15. Conti.. When an image is cropped or recompressed,the specific pattern is disrupted. Only limitation is that there is some overlap across cameras of different makes and models.

16. Double JPEG Both original and manipulated images are stored in JPEG format. In this scenario, the manipulated image is compressed twice. Double JPEG compression does not necessarily prove malicious tampering.

17. Camera-Based Techniques for modeling and estimating different camera artifacts:  Chromatic Aberration  Color Filter Array  Camera Response Inconsistencies can be used as evidence of tampering.

18. Chromatic Aberration In an ideal imaging system, light passes through the lens and is focused to a single point on the sensor. Optical system, they fail to perfectly focus light of all wavelengths. The resulting effect is known as chromatic aberration. two forms: longitudinal and lateral. In both cases, chromatic aberration leads to various forms of color imperfections in the image. Lateral aberration manifests itself as a spatial shift in the locations where light of different wavelengths reach the sensor Local lateral aberration in a tampered region is inconsistent with global aberration.

19. fig (a) fig (b)

20. Color Filter Array Digital color image consist of 3 channels (RGB). Only a single color sample is recorded at each pixel location and other two samples are estimated from the neighboring samples. If specific form of correlation is known, then it is easy to determine which pixels are correlated with their neighbors. Any deviations from this pattern is an evidence for tampering.

21. Camera Response Most digital camera sensors are linear. There should be linear relationship between the amount of light measured by each sensor element and the corresponding pixel value. Difference in response function across an image results in tampering.

22. Physics -based When image is created by splicing together individual images,there is often difficult to exactly match the lighting effects. There are techniques for estimating the properties of lighting environment. Differences in lighting across an image is the evidence of tampering.

23. Conclusion Today’s technology allows digital media to be altered and manipulated in ways that were simply impossible 20 years ago. As we continue to develop techniques for exposing photographic frauds, new techniques will be developed to make better fakes that are harder to detect. The field of image forensics, however, has made and will continue to make it harder and more time-consuming (but never impossible) to create a forgery that cannot be detected.

24. QUESTIONS ???

25. THANK YOU