• Journal of Electrical Engineering and Technology
• /
• v.10 no.4
• /
• pp.1886-1898
• /
• 2015

In today’s era of advanced technological developments, the threats to the authenticity and integrity of digital images, in a nutshell, the threats to the Image Forensics Research communities have also increased proportionately. This happened as even for the ‘non-expert’ forgers, the availability of image processing tools has become a cakewalk. This image forgery poses a great problem for judicial authorities in any context of trade and commerce. Block matching based image cloning detection system is widely researched over the last 2-3 decades but this was discouraged by higher computational complexity and more time requirement at the algorithm level. Thus, for reducing time need, various dimension reduction techniques have been employed. Since a single technique cannot cope up with all the transformations like addition of noise, blurring, intensity variation, etc. we employ multiple techniques to a single image. In this paper, we have used Fuzzy logic approach for decision making and getting a global response of all the techniques, since their individual outputs depend on various parameters. Experimental results have given enthusiastic elicitations as regards various transformations to the digital image. Hence this paper proposes Fuzzy based cloning detection and classification system. Experimental results have shown that our detection system achieves classification accuracy of 94.12%. Detection accuracy (DAR) while in case of 81×81 sized copied portion the maximum accuracy achieved is 99.17% as regards subjection to transformations like Blurring, Intensity Variation and Gaussian Noise Addition.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.31 no.6
• /
• pp.1171-1179
• /
• 2021

Image forensic is performed to check image limpidness. In this paper, a robust scheme is discussed to detect median filtering in low quality images. Detection of median filtering assists in overall image forensic. Improved spatial statistical features are extracted from the image to classify pristine and median filtered images. Image array data is rescaled to enhance the spatial statistical information. Features are extracted using Markov model on enhanced spatial statistics. Multiple difference arrays are considered in different directions for robust feature set. Further, texture operator features are combined to increase the detection accuracy and SVM binary classifier is applied to train the classification model. Experimental results are promising for images of low quality JPEG compression.

• The Journal of the Korea Contents Association
• /
• v.11 no.12
• /
• pp.552-559
• /
• 2011

In this paper, a new image watermarking algorithm is proposed which can hide specific information of an ID card's owner in photo image for preventing ID's photo forgery. Proposed algorithm uses the image segmentation and the correlation peak position modulation of spread spectrum. The watermark embedded in photo ensures not only robustness against printing and scanning but also sufficient information capacity hiding unique number such as social security numbers in small-sized photo. Another advantage of proposed method is extracting accurate information with error tolerance within some rotation range by using $2^h{\times}2^w$ unit sample space not instead $1{\times}1$ pixels for insertion and extraction of information. 40 bits information can be embedded and extracted at $256{\times}256$ sized ID photo with BER value of 0 % when the test condition is 300dpi scanner and photo printer with 22 photos. In conclusion, proposed algorithm shows the robustness for noise and rotational errors occured during printing and scanning.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.53 no.12
• /
• pp.111-119
• /
• 2016

Most commercial digital cameras acquire the colors of an image through the color filter array, and interpolate missing pixels of the image. Because of this fact, original pixels and interpolated pixels have different statistical characteristics. If colors of an image are modified, the color filter array pattern that consists of RGB channels is changed. Using this pattern change, a color forgery detection method were presented. The conventional method uses the number of pixels that exceeds the maximum or minimum value of pre-defined block by only exploiting green component. However, this algorithm cannot remove the flat area which is occurred when color is changed. And the conventional method has demerit that cannot detect the forged image with rare green pixels. In this paper, we propose an enhanced color forgery detection algorithm using the normalization and weighted sum of the color components. Our method can reduce the detection error by using all color components and removing flat area. Through simulations, we observe that our proposed method shows better detection performance compared to the conventional method.

• Journal of Korea Multimedia Society
• /
• v.8 no.11
• /
• pp.1510-1519
• /
• 2005

We proposed a digital watermarking method that it is possible to identify the copyright because the watermark is detected in the first print-scan and to protect a forgery because the watermark is not detected in the second print-scan. The proposed algorithm uses LPM and DFT transform for the robustness to the distortion of pixel value and geometrical distortion. This methods could improve watermark detection performance and image quality by selecting maximum sampling radius in LPM transform. After analyzing the characteristics of print-scan process, we inserted the watermark in the experimentally selected frequency bands that survives robustly to the first print-scan and is not detected in the second print-scan, using the characteristic of relatively large distortion in high frequency bands of DFT As the experimental result, the original proof is possible because average similarity degree 5.13 is more than the critical value 4.0 in the first print-scan. And the detection of forgery image is also possible because average similarity degree 2.76 is less than the critical value 4.0 in the second print-scan.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.9
• /
• pp.111-118
• /
• 2014

This paper presents an efficient Markov feature extraction method for detecting splicing forged images. The Markov states used in our method are composed of the difference between DCT coefficients in the adjacent blocks. Various first-order Markov state transition probabilities are extracted as features for splicing detection. In addition, we propose a feature reduction algorithm by analysing the distribution of the Markov probability. After training the extracted feature vectors using the SVM classifier, we determine whether the presence of the image splicing forgery. Experimental results verify that the proposed method shows good detection performance with a smaller number of features compared to existing methods.

• Journal of Korea Multimedia Society
• /
• v.15 no.2
• /
• pp.179-194
• /
• 2012

Digital image forgery is becoming a topic of great interest with regard to honesty in imaging. We can often see forged digital images in a variety of places, such as the internet, and magazines, and in images used in political ads, etc. These can reduce the reliability and factual basis of the information contained in image. Therefore, objectivity is needed to determine if the image is forged so as to prevent confusion in the viewing public. Most digital forgeries consist of image resizing, rotating including the following interpolations. To find evidence of interpolation in forged images, this paper proposes a new method for detecting digital image forgery using general interpolation factors analyzed through re-interpolation algorithm of the forged images in order to determine the differences in the patterns. Through the re-interpolation algorithm we could detect the forged region and filtering region used image retouching included to interpolation.

• Journal of Korea Society of Industrial Information Systems
• /
• v.23 no.5
• /
• pp.9-17
• /
• 2018

Recently, the increasing interest of biometric systems has led to the creation of many researches of biometrics forgery. In order to solve this forgery problem, this paper proposes a method of determining whether a synthesized face made of artificaial intelligence is real face or fake face. The proposed algorithm consists of two steps. Firstly, we create the fake face images using various GAN (Generative Adversarial Networks) algorithms. After that, deep learning algorithm can classify the real face image and the generated face image. The experimental results shows that the proposed algorithm can detect the fake face image which looks like the real face. Also, we obtained the classification accuracy of 88.7%.

• The KIPS Transactions:PartD
• /
• v.11D no.6
• /
• pp.1335-1340
• /
• 2004

This study introduces a technology utilizing digital images as electronic money by inserting watermark into the images. Watermarking technology assign contents ID to images and inserts the contents ID into the images in an unnoticeable way. The server that manages the issue and the usage of mage electronic money (called ‘WaterCash’ hereafter) stores issued contents ID to database and manage them as electronic money. WaterCash guarantees anonymity and prevents the forgery and modification of WaterCash based on semi-fragile watermarking technique. In addition, WaterCash is transferable and the illegal use of WaterCash can be prevented based on the watermarking technology. The watermarking .technology used in this paper was designed to be robust to image compression but vulnerable to intentional or non-intentional Image processing.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.4
• /
• pp.833-839
• /
• 2016

In this paper, we propose a selective feature extraction algorithm between Markov transition probability and co-occurrence probability for an effective image splicing detection. The Features used in our method are composed of the difference values between DCT coefficients in the adjacent blocks and the value of Kullback-Leibler divergence(KLD) is calculated to evaluate the differences between the distribution of original image features and spliced image features. KLD value is an efficient measure for selecting Markov feature or Co-occurrence feature because KLD shows non-similarity of the two distributions. After training the extracted feature vectors using the SVM classifier, we determine whether the presence of the image splicing forgery. To verify our algorithm we used grid search and 6-folds cross-validation. Based on the experimental results it shows that the proposed method has good detection performance with a limited number of features compared to conventional methods.