# Enhancing Efficiency and Performance in Deepfake Audio Detection through Neuron-level Dropin & Neuroplasticity Mechanisms

arXiv: https://arxiv.org/abs/2603.24343 · HTML: https://mlsanity.com/paper/2603.24343/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 38 references that are in the map.
- First posted on arXiv 25 Mar 2026.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models](https://mlsanity.com/paper/2311.07919/) (2023)
- [Inverse Scaling: When Bigger Isn't Better](https://mlsanity.com/paper/2306.09479/) (2023)
- [AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks](https://mlsanity.com/paper/2110.01200/) (2021)
- [End-to-end anti-spoofing with RawNet2](https://mlsanity.com/paper/2011.01108/) (2020)
- [Differentiable plasticity: training plastic neural networks with backpropagation](https://mlsanity.com/paper/1804.02464/) (2018)
- [GradMax: Growing Neural Networks using Gradient Information](https://mlsanity.com/paper/2201.05125/) (2022)
- [Hebbian Deep Learning Without Feedback](https://mlsanity.com/paper/2209.11883/) (2022)
- [Brain-inspired learning in artificial neural networks: a review](https://mlsanity.com/paper/2305.11252/) (2023)
- [When, where, and how to add new neurons to ANNs](https://mlsanity.com/paper/2202.08539/) (2022)
- [ALLM4ADD: Unlocking the Capabilities of Audio Large Language Models for Audio Deepfake Detection](https://mlsanity.com/paper/2505.11079/) (2025)
- [ExHuBERT: Enhancing HuBERT Through Block Extension and Fine-Tuning on 37 Emotion Datasets](https://mlsanity.com/paper/2406.10275/) (2024)
- [From Audio Deepfake Detection to AI-Generated Music Detection -- A Pathway and Overview](https://mlsanity.com/paper/2412.00571/) (2024)
- [Unsupervised representation learning with Hebbian synaptic and structural plasticity in brain-like feedforward neural networks](https://mlsanity.com/paper/2406.04733/) (2024)
- [Deepfake Audio Detection Using Spectrogram-based Feature and Ensemble of Deep Learning Models](https://mlsanity.com/paper/2407.01777/) (2024)
- [The NN-Stacking: Feature weighted linear stacking through neural networks](https://mlsanity.com/paper/1906.09735/) (2019)
- [Neuroplasticity in Artificial Intelligence -- An Overview and Inspirations on Drop In & Out Learning](https://mlsanity.com/paper/2503.21419/) (2025)
- [DFALLM: Achieving Generalizable Multitask Deepfake Detection by Optimizing Audio LLM Components](https://mlsanity.com/paper/2512.08403/) (2025)
- [Brain-Inspired Efficient Pruning: Exploiting Criticality in Spiking Neural Networks](https://mlsanity.com/paper/2311.16141/) (2023)
- [Learning in Deep Neural Networks Using a Biologically Inspired Optimizer](https://mlsanity.com/paper/2104.11604/) (2021)
- [Lightweight Resolution-Aware Audio Deepfake Detection via Cross-Scale Attention and Consistency Learning](https://mlsanity.com/paper/2601.06560/) (2026)

## Abstract (arXiv)

Current audio deepfake detection has achieved remarkable performance using diverse deep learning architectures such as ResNet, and has seen further improvements with the introduction of large models (LMs) like Wav2Vec. The success of large language models (LLMs) further demonstrates the benefits of scaling model parameters, but also highlights one bottleneck where performance gains are constrained by parameter counts. Simply stacking additional layers, as done in current LLMs, is computationally expensive and requires full retraining. Furthermore, existing low-rank adaptation methods are primarily applied to attention-based architectures, which limits their scope. Inspired by the neuronal plasticity observed in mammalian brains, we propose novel algorithms, dropin and further plasticity, that dynamically adjust the number of neurons in certain layers to flexibly modulate model parameters. We evaluate these algorithms on multiple architectures, including ResNet, Gated Recurrent Neural Networks, and Wav2Vec. Experimental results using the widely recognised ASVSpoof2019 LA, PA, and FakeorReal dataset demonstrate consistent improvements in computational efficiency with the dropin approach and a maximum of around 39% and 66% relative reduction in Equal Error Rate with the dropin and plasticity approach among these dataset, respectively. The code and supplementary material are available at Github link.