Ustubioglu, Beste; Küçükuğurlu, Büşranur; Ulutas, Guzin

Copy-move forgery is one of the most widely used methods in the field of audio forensic because it is difficult to detect and convenient to apply. However, post-processing operations applied to forged speech to hide traces of forgery make detection of forensic more difficult. In this study, we propose a robust method for detection and localization of the audio copy-move forgery using modified discrete cosine transform (MDCT). For this purpose, we first divide the speech recording into voiced and unvoiced parts by extracting a pitch sequence from the speech recording. After the determination of voiced parts, we extract MDCT coefficients from them and take the mean of the transpose of the coefficient matrix as the feature. These MDCT features are very robust against commonly used post-processing operations (especially audio compressing). Euclidean distance (ED) is applied to compute the similarities between the features. The voiced parts, which give minimum ED are determined as copy-move audio parts. Also, two separate databases (Pitch-based and Voice Activity Detection (VAD-based) are created during the work to evaluate the performance of the proposed method because there is no common database in the field of audio forensic in the literature. Experiment results show that the proposed method gives better results for detection and localization of audio copy-move forgery on different databases compared to other studies in the literature. The proposed method is also robust against common post-processing operations such as noise addition, filtering operation, and especially compression operation.

Kucukugurlu, Busranur; Ustubioglu, Beste; Ulutas, Guzin; Herencsar, N.

Due to the advantages of convenience, and difficulty of detection, copy-move forgery is one of the most common audio forgery forms. It is significant to decide whether there is a duplicated segment in the audio. In order to detect copy-move forgery, a new method is proposed in this paper. In the proposed algorithm, first the audio is segmented into syllables using a pitch tracking method. Second, each syllable analyzed with a 1-D local binary pattern operator. This operator calculates the histogram of each syllable. Mean square error is used to compute the similarities of histograms. The syllables which have similar histograms are detected as forged audio segments. Compared to another 1-D local binary pattern method, the proposed method has better detection results for copy-move forgery. Our experiments show that the proposed algorithm is feasible and effective, and robust against many common postprocessing operations such as noise addition, filtering, and compression.

Sahin, Yasin; Ulutas, Guzin; Imamoglu, Mustafa Bilgehan

Protecting database integrity has become a matter of increasing importance nowadays as accessing information is getting easier. Therefore, database watermarking approach to protect the integrity become an active research area. Most of the existing relational database watermarking techniques embed a generated watermark in the database. Zero watermarking is employed in the proposed technique by storing the watermark generated from the database in another medium independent from the database. In order to make the method robust to column replacement attacks, cell values are split into overlapping pairs and column number is added to pairs. The histogram of pairs is used as the watermark because any change in either a cell value or column replacement would affect the histogram. Experimental results indicate robustness to column replacement attack as well as improved processing time compared to other works reported in the literature.

Türk, Salih; Bingöl, Ozkan; Ulutas, Guzin

Copy-move forgery is performed as a part of an image is copied and moved to another region within the same image. For the detection of this type of forgery, usually, feature vectors of the image are created by dividing the image into sub-blocks. Then the feature vector of the region is compared with other regions in the image to reach a conclusion if they are matched. In this study, to determine the copy-move forgery, DoG Coding technique which is more resistant to changes in image brightness is used. Coded new image is formed with the help of this technique often preferred in the palmprint recognition systems and features are determined by dividing the image into sub-blocks. Block based region growing segmentation method have been developed to determine the size of the copied region. Proposed this new approach has been successfully applied on images in the CoMoFod dataset prepared by different scenarios.