# Noise Modeling, Synthesis and Classification for Generic Object Anti-Spoofing

arXiv: https://arxiv.org/abs/2003.13043 · HTML: https://mlsanity.com/paper/2003.13043/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2020.

## Built on by (most cited first, 14 of 14)

- [DCFace: Synthetic Face Generation with Dual Condition Diffusion Model](https://mlsanity.com/paper/2304.07060/) (2023)
- [Deep Learning for Face Anti-Spoofing: A Survey](https://mlsanity.com/paper/2106.14948/) (2021)
- [Rethinking Domain Generalization for Face Anti-spoofing: Separability and Alignment](https://mlsanity.com/paper/2303.13662/) (2023)
- [Multi-domain Learning for Updating Face Anti-spoofing Models](https://mlsanity.com/paper/2208.11148/) (2022)
- [Unified Detection of Digital and Physical Face Attacks](https://mlsanity.com/paper/2104.02156/) (2021)
- [Feature Generation and Hypothesis Verification for Reliable Face Anti-Spoofing](https://mlsanity.com/paper/2112.14894/) (2021)
- [FM-ViT: Flexible Modal Vision Transformers for Face Anti-Spoofing](https://mlsanity.com/paper/2305.03277/) (2023)
- [Surveillance Face Anti-spoofing](https://mlsanity.com/paper/2301.00975/) (2023)
- [InstructFLIP: Exploring Unified Vision-Language Model for Face Anti-spoofing](https://mlsanity.com/paper/2507.12060/) (2025)
- [Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results](https://mlsanity.com/paper/2304.05753/) (2023)
- [Improved Detection of Face Presentation Attacks Using Image Decomposition](https://mlsanity.com/paper/2103.12201/) (2021)
- [MToFNet: Object Anti-Spoofing with Mobile Time-of-Flight Data](https://mlsanity.com/paper/2110.04066/) (2021)
- [Multi-Frames Temporal Abnormal Clues Learning Method for Face Anti-Spoofing](https://mlsanity.com/paper/2208.04076/) (2022)
- [Receipt Replay OOD: A Small Benchmark for Screen Replay Detection Under Domain Shift](https://mlsanity.com/paper/2605.26855/) (2026)

## Abstract (arXiv)

Using printed photograph and replaying videos of biometric modalities, such as iris, fingerprint and face, are common attacks to fool the recognition systems for granting access as the genuine user. With the growing online person-to-person shopping (e.g., Ebay and Craigslist), such attacks also threaten those services, where the online photo illustration might not be captured from real items but from paper or digital screen. Thus, the study of anti-spoofing should be extended from modality-specific solutions to generic-object-based ones. In this work, we define and tackle the problem of Generic Object Anti-Spoofing (GOAS) for the first time. One significant cue to detect these attacks is the noise patterns introduced by the capture sensors and spoof mediums. Different sensor/medium combinations can result in diverse noise patterns. We propose a GAN-based architecture to synthesize and identify the noise patterns from seen and unseen medium/sensor combinations. We show that the procedure of synthesis and identification are mutually beneficial. We further demonstrate the learned GOAS models can directly contribute to modality-specific anti-spoofing without domain transfer. The code and GOSet dataset are available at cvlab.cse.msu.edu/project-goas.html.