Determining the origin of a digital image or video, namely device source identification, is widely used in courtroom evidence and copyright protection. Currently, device source identification primarily focuses on images captured using single camera with default settings. However, with the advancement of imaging technology, there is a large number of smartphones equipped with multiple cameras and various shooting modes for acquiring images, which may pose a significant challenge to device source identification. Therefore, to assess the performance of image source identification algorithm for modern smartphones and promote further research, it is crucial to build a dataset of image and video captured by modern smartphones. In this paper, we present a large-scale image and video dataset for forensic analysis, ForensiCam-215K. The dataset includes over 215K media contents captured by 130 modern smartphones of 10 major brands. We used the latest equipment to capture images from the main, wide-angle, and telephoto cameras in six different shooting modes, and the media were collected under a strictly controlled procedure to reduce the bias caused by differences in the acquisition process between different devices. Additionally, we used the Photo Response Non-Uniformity (PRNU) method to perform device source identification tests on the dataset. The results indicate that device source identification is a challenging task especially for images and videos captured by smartphones with multiple cameras and various shooting modes. The dataset will be released as open-source and freely available for use by the multimedia forensics research community at https://github.com/dswdsw21072/ForensiCam-215K.

Due to the development of facial manipulation technologies, the generated deepfake videos cause a severe trust crisis in society. Existing methods prove that effective extraction of the artifacts introduced during the forgery process is essential for deepfake detection. However, since the features extracted by supervised binary classification contain a lot of artifact-irrelevant information, existing algorithms suffer severe performance degradation in the case of the mismatch between training and testing datasets. To overcome this issue, we propose an Artifacts-Disentangled Adversarial Learning (ADAL) framework to achieve accurate deepfake detection by disentangling the artifacts from irrelevant information. Furthermore, the proposed algorithm provides visual evidence by effectively estimating artifacts. Specifically, Multi-scale Feature Separator (MFS) in the disentanglement generator is designed to precisely transmit the artifact features and optimize the connection between the encoder and decoder. In addition, we design an Artifacts Cycle Consistency Loss (ACCL) which uses the disentangled artifacts to construct new samples and enables pixel-level supervised training for the generator to estimate more accurate artifacts. The symmetric discriminators are paralleled to differentiate the constructed samples from the original images in both fake and real domains, making the adversarial training process more stable. Extensive experiments on existing benchmarks demonstrate that the proposed method outperforms the state-of-the-art approaches.

In the multimedia forensics community, anti-forensics of contrast enhancement (CE) in digital images is an important topic to understand the vulnerability of the corresponding CE forensic method. Some traditional CE anti-forensic methods have demonstrated their effective forging ability to erase forensic fingerprints of the contrast-enhanced image in histogram and even gray level cooccurrence matrix (GLCM), while they ignore the problem that their ways of pixel value changes can expose them in the pixel domain. In this paper, we focus on the study of CE anti-forensics based on Generative Adversarial Network (GAN) to handle the problem mentioned above. Firstly, we exploit GAN to process the contrast-enhanced image and make it indistinguishable from the unaltered one in the pixel domain. Secondly, we introduce a specially designed histogram-based loss to enhance the attack effectiveness in the histogram domain and the GLCM domain. Thirdly, we use a pixel-wise loss to keep the visual enhancement effect of the processed image. The experimental results show that our method achieves high anti-forensic attack performance against CE detectors in the pixel domain, the histogram domain, and the GLCM domain, respectively, and maintains the highest image quality compared with traditional CE anti-forensic methods.