(IJCSIS) International Journal of Computer Science and Information Security, Vol. 8 No. 8, 2010 Retrieval of Bitmap Compression History Salma Hamdy, Haytham El-Messiry, Mohamed Roushdy, Essam Kahlifa Faculty of Computer and Information Sciences Ain Shams University Cairo, Egypt {s.hamdy, hmessiry, mroushdy, esskhalifa}@cis.asu.edu.eg Abstract —The histogram of Discrete Cosine Transform coefficients contains information on the compression parameters for JPEGs and previously JPEG compressed bitmaps. In this paper we extend the work in [ 1] to identify previously compressed bitmaps and estimate the quantization table that was used for compression, from the peaks of the histogram of DCT coefficients. This can help in establishing bitmap compression history which is particularly useful in applications like image authentication, JPEG artifact removal, and JPEG recompression with less distortion. Furthermore, the estimated table calculates distortion measures to classify the bitmap as genuine or forged. The method shows good average estimation accuracy of around 92.88% against MLE and autocorrelation methods. In addition, because bitmaps do not experience data loss, detecting inconsistencies becomes easier. Detection performance resulted in an average false negative rate of 3.81% and 2.26% for two distortion measures, respectively. Keywords: Digital image forensics; forgery detection; compression history; Quantization tables. I.INTRODUCTIONAlthough JPEG images are the most widely used image format, sometimes images are saved in an uncompressed raster form (bmp, tiff), and in most situations, no knowledge ofprevious processing is available. Some applications are required to receive images as bitmaps with instructions for rendering at a particular size and without further information. The image may have been processed and perhaps compressed with contain severe compression artifacts. Hence, it is useful to determine the bitmap history; whether the image has ever been compressed using the JPEG standard and to know what quantization tables were used. Most of the artifact removal algorithms [2-9] require the knowledge of the quantization table to estimate the amount of distortion caused by quantization and avoid over-blurring. In other applications, knowing the quantization table can help in avoiding further distortion when recompressing the image. Some methods try to identify bitmap compression history using Maximum Likelihood Estimation (MLE) [ 10-11] or by modeling the distribution of quantized DCT coefficients, like the use ofBenford’s law [12], or modeling acquisition devices [ 13]. Furthermore, due to the nature of digital media and the advanced digital image processing techniques, digital images may be altered and redistributed very easily forming a rising threat in the public domain. Hence, ensuring that media content is credible and has not been altered is becoming an important issue governmental security and commercial applications. As a result, research is being conducted for developing authentication methods and tamper detection techniques. Usually JPEG compression introduces blocking artifacts and hence one of the standard passive approaches is to use inconsistencies in these blocking fingerprints as a reliable indicator of possible tampering [ 14]. These can also be used to determine what method of forgery was used. In this paper we are interested in the authenticity of the image. We extend the work in [ 1] to bitmaps and use the proposed method for identifying previously compressed bitmaps and estimating the quantization table that was used. The estimated table is then used to determine if the mage was forged or not by calculating distortion measures. In section 2 we study the histogram of DCT AC coefficients of bitmaps and show how it differs for previously JPEG compressed bitmaps. We then validate that without modeling rounding errors or calculating prior probabilities, quantization steps of previously compressed bitmaps can still be determined straightforward from the peaks of the approximated histograms of DCT coefficients. Results are discussed in section 3. Section 4 is for conclusions. II.HISTOGRAM OF DCTCOEFFICIENTS IN BITMAPSWe studied in [ 1] the histogram of quantized DCT coefficients and showed how it can be used to estimate quantization steps. Here, we study uncompressed images and validate that the approximated histogram of DCT coefficients can be used to determine compression history. Bitmap image means no data loss and hence all what is required to build an informative histogram is expected to be present in the coefficients histograms. The first step is to decide if the test image was previously compressed because if the image was an original uncompressed there is no compression data to extract. When the image is decided to have a compression history, the next step is to estimate that history. For grayscale image, compression history mainly means its quantization table which will be the focus of this paper. For color image, this is extended to estimating color plane compression parameters that includes subsampling and associated interpolation . 141 http://sites.google.com/site/ijcsis/ ISSN 1947-5500
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IV. CONCLUSIONS
The method discussed in this paper is based on using theapproximated histogram of DCT coefficients of bitmaps forextracting the image’s compression history; its quantizationtable. Also the extracted table is used to expose imageforgeries. The method proved to have practically highestimation accuracy when tested on a large set of image fromdifferent sources compared to other statistical approaches.
Moreover, estimation times proved to be faster than statisticalmethods while maintaining very good accuracy for lowerfrequencies. Experimental results also showed thatperformance for bitmaps surpasses that of JPEGs because of their lossy nature but on the other hand, it takes more time toprocess a bitmap.
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(a) Three composite bitmap images.
(b) Distortion measure for the three images in (a).
Fig. 5. Distortion measures for some composite bitmap images. The left panel represents the average distortion measure while the right panel represents the