Abstract: Most of existing deep learning-based methods for image splicing forgery detection use convolutional layer for forensics feature extraction.However,convolution kernel conducts the local computation process with the limited reception field.More-over,existing methods mainly apply the location of tampering regions to guide the detection model to train,and it is difficult to learn richer tamper trace features.To overcome above-mentioned limitations,a multitask transformer-based network(MT-Net) is proposed for image splicing detection and localization.The self-attention mechanism of Transformer is leveraged in encoder to learn the pixel correlation,which is able to provide different attention levels for pixels and makes the detection network pay more attention to tampering traces.Meanwhile,MT-Net considers three subtasks simultaneously to guide the detection network expose tampering traces from both local and global information,including tampered edge detection,tampered area detection and the prediction of the tampered area's proportion.Finally,three specific loss functions for their corresponding subtask are designed to better optimize the detection network in the training phase.In experiments,the proposed method(MT-Net) achieves better detection results compared with other state-of-the-art methods on three public available datasets,including CASIA v2.0,Columbia and IDM2020,where F1 scores are 0.808,0.913 and 0.675 respectively.The visualization results also demonstrate that the proposed method has the better capability of localizing the splicing regions.

Key words: Digital image forensics, Image splicing detection, Transformer, Self-attention mechanism, Multitask network