# RawTFNet: A Lightweight CNN Architecture for Speech Anti-spoofing

arXiv: https://arxiv.org/abs/2507.08227 · HTML: https://mlsanity.com/paper/2507.08227/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2025.

## Built on by (most cited first, 4 of 4)

- [Speech DF Arena: A Leaderboard for Speech DeepFake Detection Models](https://mlsanity.com/paper/2509.02859/) (2025)
- [Teffic-Audio: Tell Fact from Fiction](https://mlsanity.com/paper/2607.28351/) (2026)
- [Tracing Decoder Artifacts for Compact Synthetic Speech Screening](https://mlsanity.com/paper/2609.32050/) (2026)
- [Adaptive Compression and Targeted Perturbation: A Unified Framework for Generalized Audio Deepfake Detection](https://mlsanity.com/paper/2610.16049/) (2026)

## Abstract (arXiv)

Automatic speaker verification (ASV) systems are often affected by spoofing attacks. Recent transformer-based models have improved anti-spoofing performance by learning strong feature representations. However, these models usually need high computing power. To address this, we introduce RawTFNet, a lightweight CNN model designed for audio signals. The RawTFNet separates feature processing along time and frequency dimensions, which helps to capture the fine-grained details of synthetic speech. We tested RawTFNet on the ASVspoof 2021 LA and DF evaluation datasets. The results show that RawTFNet reaches comparable performance to that of the state-of-the-art models, while also using fewer computing resources. The code and models will be made publicly available.