Sensor pattern noise (SPN) extraction is a critical stage of the sensor based source camera identification (SCI). However, the quality of the extracted SPN with the traditional discrete wavelet transform (DWT) based method is poor around strong edges and along with the image border. To fill this gap, we propose a dual tree complex wavelet transform (DTCWT) based method to extract the SPN from a given image, which achieves better performance in the area around strong edges. Furthermore, symmetric boundary extension instead of the periodized boundary extension is used for enhancing the quality of SPN along with the image border. Extensive experimental results on both synthetic noisy images and real-world photographs clearly demonstrate the superior SCI performance of the proposed method over state-of-the-arts. Moreover, the proposed method also shows potential in the application of image tampering localization.

The statistical fingerprints left by median filtering can be a valuable clue for image forensics. However, these fingerprints may be maliciously erased by a forger. Recently, a tricky anti-forensic method has been proposed to remove median filtering traces by restoring images' pixel difference distribution. In this paper, we analyze the traces of this anti-forensic technique and propose a novel counter method. The experimental results show that our method could reveal this anti-forensics effectively at low computation load. According to our best knowledge, it's the first work on countering anti-forensics of median filtering.