Digital Image Passive Forensics Based On Quantization Table And CFA Interpolation

Images play an important role in information transmission. With the popularity of digital image processing tools, it is becoming easier to tamper a digital image without leaving obvious traces. Tampered images may be bad influences to our society. Therefore, authentication of digital images has increasingly become a hot topic in the information security field.Many JPEG images may be saved as bitmap format after being tampered, and they may employ different quantization tables. Therefore, it is very important for digital image forensics to identify whether a bitmap has been JPEG compressed, if so, to further estimate its quantization table. JPEG images sometimes are recompressed after tampering. Thus there is a need to identify whether a JPEG image has been double compressed. Most of cameras employ a single CCD, the original recorded image data are to be interpolated into a color image. Interpolation algorithms differ among cameras. Therefore, image source identification can also be achieved by detecting interpolation algorithm. Our work includes:First, the Image Characteristic Matrix (ICM) is defined, and then a method for JPEG detection and double compression detection is proposed. The number of DCT coefficient which equal to zero will be dramatically changed after compression. The testing image is first cropped in64ways and these cropped images are then DCT transformed. Counting the coefficients which equal to zero for each of the transformed images and the8×8ICM are obtained. ICM would be changed by compression or recompression. S1and S2, both of which are derived from ICM, can be used in JPEG detecting and double recompression detecting respectively. At the same time, the location of block boundary can be localized through the location of the maximum of the ICM.Secondly, this paper improves the current quantization step estimation algorithm, and proposes two novel algorithms to estimate quantization table. Current quantization step estimation algorithm is based on the histogram of DCT coefficients. This paper exploits the fact that the multiples of the quantization step are local maximum in the DCT histogram, and improves the current algorithm. Improve algorithm can estimate long quantization step well. The estimated quantization table obtained from estimating every single quantization step may contain incorrect estimated quantization steps. The half-baked quantization table is matched with all has-known quantization tables and the best matched quantization table is chose. Based on the observation that quantization table relates more close to the blocks quantized by the table, the correlation between the image and all quantization tables is calculated and the correct table is ultimately estimated. In this paper, the estimated table is future used to localize the tampered regions.Finally, the CFA interpolation algorithm detection is studied. Most of inner channel interpolation algorithms will never interpolate local maximum or minimum. In this paper, the proposed algorithm identifies whether an image employs Bayer pattern through the location of maximum and minimum of image data. When an image is re-interpolated, the re-interpolated image which employs the same interpolation algorithm of the original image is more close to the original image. Based on such observation, interpolation algorithm is estimated by re-interpolating image. The testing image is first re-interpolated by all interpolation algorithms, the best match with the testing image is chosen. The interpolation algorithm correlated to the chosen image is the estimated interpolation algorithm.Experiments in chapter three to five show the effectiveness of the proposed JPEG detection, double compression detection, quantization steps and quantization table estimation, quantization table-based tampered region localization, Bayer pattern detection and CFA interpolation algorithm estimation algorithm.