Image Size Alteration Detection Research

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1 Image Size Alteration Detection Research Christopher Mancini Professor Charles Boncelet December 5, 2011

2 ABSTRACT This research was conducted in order to construct a coding algorithm that can detect image resizing from image editing software. This algorithm can be incorporated with current programs that are used to detect the authenticity of images. The constructed program takes advantage of the interpolation methods that are used when image editing software resize an image. The images are analyzed at the pixel level, where individual pixels in an image are compared to a block of pixels which surround that individual pixel. For images that have been resized, the pixels do not vary in value with their surrounding pixels as much as the pixels in unaltered images. The results of this study show that the program is highly effective with images taken indoors but needs more work in order to predict images taken outside. OBJECTIVE Not all photographs that are seen in magazines, books, or on the internet are directly from someone s camera. The growing availability of image editing software and its capabilities today have allowed people to easily make changes to an image, such as altering its lighting, contrast, or size. While these programs allow photographers to improve the quality of their work, they also complicate the issue of determining the authenticity of photographs. Changes made in image editing software will not necessarily be visible to the naked eye; therefore, photograph analyzers need to examine images at the pixel-level in order to make distinctions.

3 This research specifically focuses on detecting image resizing by analyzing the values of individual pixels that comprise the images. A programming algorithm was constructed to analyze patterns between unaltered and resized images, and then test images for image resizing based on these patterns. This program only looks at one aspect of image alteration, but can be a valuable addition to current algorithms which are used in this field. BACKGROUND INFORMATION In order to distinguish between original and resized photos, it is first necessary to understand how image editing software resizes images. The image editing software used in this study was MatLab (version R2008a) and Photoshop (version CS4). For both of these programs, some form of interpolation is used when resizing images. When scaling an image down, the image can no longer fit all its original pixels, so it must lose pixel information. Blocks of surrounding pixels from the original image are averaged together to form single pixels for the new image. When scaling an image up, the image needs more pixels to fill, so it must gain pixel information. Each pixel that needs to be filled in for the new image is an average of the surrounding block of pixels from the original image. Therefore, if this averaging can be detected, the authenticity of the image can be determined.

4 RESEARCH CONDUCTED In order to eliminate as many variables as possible, all the photographs used in this research came from one camera. Using a Canon PowerShot SX1 IS, one hundred photos were taken using various zoom magnifications. Fifty of the pictures were taken outside in daylight and fifty were taken inside using artificial light. All of these photos were then converted to a.jpg file format and classified as unaltered. A copy of these images was made to be resized using MatLab and Photoshop. Half of the outside pictures and half of the inside pictures were set aside to be altered by MatLab, while the remaining pictures would be altered by Photoshop. The altered photos were resized using their respective image editing software by first one half, then by two. Then both the original images and altered images were converted to grayscale using MatLab so that each image would only have one color channel. A program in MatLab was constructed next, comparing the original images to the altered images. For each image, the program plots a histogram of the subtraction between each individual pixel value in an image and the average value of a block of pixels that surround it. This average is weighted based on the distance away from the pixel being analyzed; pixels closest to the target pixel contribute more to the average than those which are further away. The plot revealed that the pixels in the altered images tended to be more similar to its surrounding pixels than those in the original images (See PLOT 1 below). This pattern is due to the averaging of pixel values that occurs when images are resized using interpolation. Multiple computed averages for the program were tested to determine which would produce the greatest difference between altered and original images.

5 Before constructing the image testing algorithm, a matched filter was used to determine a threshold value that would be used to distinguish between the original and altered images. The matched filter compares each image histogram to the average histogram of both the original and altered images. Images with a matched filter value above the threshold value would be determined as altered, while those below the threshold would be determined as original. The optimal threshold value was found by setting a value that would correctly identify original images by at least ninety five percent, while still identifying altered images at a high rate. With the optimal computed average and threshold value set, the image testing algorithm was constructed. One hundred and eighty new photographs were taken using the same camera, equally divided between outside and inside photos, which were then equally separated into those that would and would not be altered in size. The conducted tests analyzed altered images which were scaled down, then up by a factor of two and by a factor of four. The histogram from each image was computed using the optimal computed average (See PLOT 2 below). The matched filter was applied to each histogram, and the output values were then compared to the threshold value to determine the expected nature of the image (original or altered). Finally, the program compared the expected condition of each image to its actual condition and displayed the test statistics.

6 RESULTS The statistics for the image testing program were surprising. The program was able to classify over ninety five percent of the original images correctly when the altered images were scaled by a factor two and when they were scaled by four. Considering the null hypothesis that the image is an original, this means that the true positive rate is considered high. However, the true negative rate (correctly classifying altered images) is slightly below sixty four percent for the scaling factor of two. For the scaling factor of four, the rate is higher at eighty seven and a half percent, but is still considered low. After further analysis, an interesting trend appeared. Most of the altered images that were identified incorrectly were photos taken outside. When looking at photos taken inside, the true negative rate for the scaling factor of two is considered high at ninety seven and a half percent. For the scaling factor of four, it is at one hundred percent (See TABLE 1 below). CONCLUSION Although the testing for all the images has some inaccuracies, the predictability of the image testing program for images taken inside is statistically significant. A potential reason for the inconsistency between the indoor and outdoor images is because outdoor images contain more objects which vary with color than indoor images. For example, the trees, ground, and wildlife vary with color while the walls, floor, and furniture are a fairly consistent color. Therefore, despite the averaging done when resizing, the surrounding pixels in outdoor images still vary enough so that the image is detected as an original.

7 In order to improve this research study, separate threshold values should be chosen for the inside images and for the outside images so that more outside images can be detected correctly. Another change that might improve this study would be to expand the surrounding block of pixels when computing the weighted average. The amount of surrounding pixels in this study might not have been enough to reflect the averaging that is made in Photoshop and MatLab. In order to make this study more dynamic, multiple cameras could be used, more photograph samples could be taken, and more image editing software could be looked at. With these improvements, this coding algorithm would be a valuable addition to current image forgery detection software for detecting image resizing for images taken both indoors and outdoors. FIGURES PLOT 1

8 PLOT 2 TABLE 1 Test Results True Positives True Negatives Scaling of % 63.75% Scaling of 2 (Just Inside) 97.50% 97.50% Scaling of % 87.50% Scaling of 4 (Just Inside) 97.50% %

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