An efficient Video Forgery Detection using Two-Layer Hybridized Deep CNN classifier

Video forgery detection is crucial to combat misleading content, ensuring trust and credibility. Existing methods encounter challenges such as diverse manipulation techniques, dataset variation, real-time processing demands, and maintaining a balance between false positives and negatives. The research focuses on leveraging a Two-Layer Hybridized Deep CNN classifier for the detection of video forgery. The primary objective is to enhance accuracy and efficiency in identifying manipulated content. The process commences with the collection of input data from a video database, followed by diligent data pre-processing to mitigate noise and inconsistencies. To streamline computational complexity, the research employs key frame extraction to select pivotal frames from the video. Subsequently, these key frames undergo YCrCb conversion to establish feature maps, a step that optimizes subsequent analysis. These feature maps then serve as the basis for extracting significant features, incorporating Haralick features, Local Ternary Pattern, Scale-Invariant Feature Transform (SIFT), and light coefficient features. This multifaceted approach empowers robust forgery detection. The detection is done using the proposed Two-Layer Hybridized Deep CNN classifier that identifies the forged image. The outputs are measured using accuracy, sensitivity, specificity and the proposed Two-Layer Hybridized Deep CNN achieved 96.76%, 96.67%, 96.21% for dataset 1, 96.56%, 96.79%, 96.61% for dataset 2, 95.25%, 95.76%, 95.58% for dataset 3, which is more efficient than other techniques.