Video forgery detection using competitive swarm sun flower optimization algorithm based deep learning

Nowadays, a surveillance camera is used extensively to provide security. With easier accessibility of tools like video editing, it became simple to destroy evidence. Different detection techniques are in practice howsoever due to many reasons it is confined. Hence, the competitive swarm sunflower optimization algorithm (CSSFOA)-based random multimodal deep learning (RMDL) is proposed for discovering forgeries. CSSFOA is the integration of competitive swarm optimizer and sunflower optimization. Here, extraction of the keyframe is carried out utilizing discrete cosine transform and Tanimoto distance. Also, by using the Viola-Jones algorithm, face detection is performed considering the light coefficients and face image coefficients by extracting the local optimal oriented pattern. The deep composite images are obtained utilizing RMDL. RMDL is trained utilizing developed CSSFOA. The proposed CSSFOA-based RMDL shows superior performance with maximum accuracy of 96.6%, true positive rate of 95.0%, and true negative rate of 95.5%.