# PDC-ViT : Source Camera Identification using Pixel Difference Convolution and Vision Transformer

arXiv: https://arxiv.org/abs/2501.16227 · HTML: https://mlsanity.com/paper/2501.16227/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 57 references that are in the map.
- First posted on arXiv 27 Jan 2025.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet](https://mlsanity.com/paper/2101.11986/) (2021)
- [Scaling Vision Transformers to 22 Billion Parameters](https://mlsanity.com/paper/2302.05442/) (2023)
- [Stand-Alone Self-Attention in Vision Models](https://mlsanity.com/paper/1906.05909/) (2019)
- [Searching Central Difference Convolutional Networks for Face Anti-Spoofing](https://mlsanity.com/paper/2003.04092/) (2020)
- [Pixel Difference Networks for Efficient Edge Detection](https://mlsanity.com/paper/2108.07009/) (2021)
- [TransMix: Attend to Mix for Vision Transformers](https://mlsanity.com/paper/2111.09833/) (2021)
- [TokenMix: Rethinking Image Mixing for Data Augmentation in Vision Transformers](https://mlsanity.com/paper/2207.08409/) (2022)
- [PatchDropout: Economizing Vision Transformers Using Patch Dropout](https://mlsanity.com/paper/2208.07220/) (2022)
- [Forensic Similarity for Digital Images](https://mlsanity.com/paper/1902.04684/) (2019)
- [xUnit: Learning a Spatial Activation Function for Efficient Image Restoration](https://mlsanity.com/paper/1711.06445/) (2017)
- [Deep learning for source camera identification on mobile devices](https://mlsanity.com/paper/1710.01257/) (2017)
- [Video Camera Identification from Sensor Pattern Noise with a Constrained ConvNet](https://mlsanity.com/paper/2012.06277/) (2020)
- [A Novel Hierarchical-Classification-Block Based Convolutional Neural Network for Source Camera Model Identification](https://mlsanity.com/paper/2212.04161/) (2022)

## Abstract (arXiv)

Source camera identification has emerged as a vital solution to unlock incidents involving critical cases like terrorism, violence, and other criminal activities. The ability to trace the origin of an image/video can aid law enforcement agencies in gathering evidence and constructing the timeline of events. Moreover, identifying the owner of a certain device narrows down the area of search in a criminal investigation where smartphone devices are involved. This paper proposes a new pixel-based method for source camera identification, integrating Pixel Difference Convolution (PDC) with a Vision Transformer network (ViT), and named PDC-ViT. While the PDC acts as the backbone for feature extraction by exploiting Angular PDC (APDC) and Radial PDC (RPDC). These techniques enhance the capability to capture subtle variations in pixel information, which are crucial for distinguishing between different source cameras. The second part of the methodology focuses on classification, which is based on a Vision Transformer network. Unlike traditional methods that utilize image patches directly for training the classification network, the proposed approach uniquely inputs PDC features into the Vision Transformer network. To demonstrate the effectiveness of the PDC-ViT approach, it has been assessed on five different datasets, which include various image contents and video scenes. The method has also been compared with state-of-the-art source camera identification methods. Experimental results demonstrate the effectiveness and superiority of the proposed system in terms of accuracy and robustness when compared to its competitors. For example, our proposed PDC-ViT has achieved an accuracy of 94.30%, 84%, 94.22% and 92.29% using the Vision dataset, Daxing dataset, Socrates dataset and QUFVD dataset, respectively.