Akademik Veri Yönetim Sistemi
MULTIMEDIA TOOLS AND APPLICATIONS, cilt.1, sa.1, ss.1-11, 2023 (SCI-Expanded)
Videos are one of the most substantial evidence that can be used to detect incidents. However, videos can be altered easily using current technologies. Alterations can be made for malicious purposes. Therefore, it is essential to determine the integrity of the videos that will be used as evidence or to give people the right idea. Alterations on the videos that have not previously added control data are within the scope of passive fraud detection. This study proposes an effective solution for detecting frame duplication attacks, which is one of the passive forgery types. The study is based on the visualization of feature vectors. A binary image is created with the feature matrix using feature vectors. Thus, a representative approach to the problem is presented. The forged frame-group template is obtained by processing the binary image, and then a search is done using this template. The proposed method provides solutions for both uncompressed and compressed videos. The algorithm's durability against compression has been tested by evaluating MPEG4 and H264 coded videos. A blurring attack can also be applied to the altered videos to hide the forgery. The results show that it is resistant to blurring attacks. Another factor that complicates fraud detection is the location of the forgery. The algorithm can detect forgery at the beginning or end of the video. The source of the forged frames can also be detected in the study. Experimental results show that the algorithm is resistant to compression, fast, and has a high accuracy rate.