Detection-Identification Balancing Margin Loss For One-Stage Multi-Object Tracking

Identification of source social networks of images based on the traces left on images by such platforms is a crucial task in image forensics. The existing techniques provide successful solutions to such a problem. However, we show that the state-of-the-art techniques are adversely affected due to the leaking side-channel information from scene details that convolutional neural networks (CNNs) are prone to exploit. Thus, highly correlated scene details in the training and test sets lead to overestimation of the performance. To address this problem, we develop a data-driven system by parallelizing three CNNs having kernels with different sizes that benefit from learning more relevant forensic traces making the model less susceptible to scene content. The experimental results achieved by the proposed model either trained on images with or without scene overlap show that there is no influence of scene content in the feature learning of the proposed method.