查看文章

The dynamic nature of video content facilitates steganographic processes, and recent advancements in Deep Learning, particularly Convolutional Neural Networks (CNNs), have led to the development of new steganographic techniques. The main contribution lies in using CNN-based deep learning techniques for object identification, with a particular focus on leveraging the full potential of ResNet and VGGNet architectures to achieve superior object classification results. In this paper, StegVRN-a deep learning model for object detection and data embedding using edge detection technique is proposed. StegVRN comprises 2 models: the first model, VEDS (VGGNet-based Edge Detection Steganography), employs the VGGNet architecture for object detection and utilizes an edge detection algorithm for data embedding. The second model, named REDS (ResNet-based Edge Detection Steganography), is based on …