EnvSSLAM-FFN: Lightweight Layer-Fused System for ESDD 2026 Challenge

In this paper, we propose a deep-learning framework for Environmental Sound Deepfake Detection (ESDD) - the task of identifying whether the sound scene and sound event in an input audio recording is fake or real. To this end, we first conduct extensive experiments to explore how individual spectrograms, a wide range of network architectures, and pre-trained models affect the performance of an ESDD model. The experimental results on the benchmark datasets of EnvSDD indicate that detecting deepfake audio of sound scenes and detecting deepfake audio of sound events should be considered as individual tasks. We also show that fine-tuning a pre-trained model is more effective than training a model from scratch for ESDD. Ultimately, our best model, which fine-tunes the pre-trained BEATs model using the proposed two-phase training strategy, achieves an Accuracy of 0.98, F1 score of 0.95, and AUC score of 0.99 on the Test subset of the EnvSDD dataset. Our best model also achieves an Accuracy of 0.86, F1 score of 0.80, and AUC of 0.93 when evaluated cross-dataset on the ESD-Challenge-TestSet dataset.

The Environment-Aware Speech and Sound Deepfake Detection Challenge (ESDD2), held in conjunction with ICME 2026, evaluated systems for five component-level audio spoofing detection, where speech and environmental sounds may be manipulated independently or jointly. After the challenge concludes, we analyze the final leaderboard and summarize effective design choices from the top-performing submissions. The challenge attracted 94 registrations from 16 countries; after verification of submission requirements and metadata, 13 teams were retained for the final analysis. On the test set, the best system achieved a Macro-F1 score of 0.8775, substantially outperforming the separation-enhanced joint learning baseline (0.6327). Top systems consistently benefited from modular task decomposition, cross-domain self-supervised encoders, targeted data augmentation, and selective ensembling rather than simple model scaling. At the same time, auxiliary EER analyses reveal persistent difficulty in detecting the spoofed environmental component and in generalizing to unseen generators in the test set. This paper reports challenge results and provides insights for future environment-aware deepfake detection research. The CompSpoofV2 dataset and baseline code remain publicly available for reproducibility.

This paper describes a submission to the Environment-Aware Speech and Sound Deepfake Detection Challenge (ESDD2) 2026, which addresses component-level deepfake detection using the CompSpoofV2 dataset, where speech and environmental sounds may be independently manipulated. To address this challenge, a dual-branch deepfake detection framework is proposed to jointly model speech and environmental contextual representations from input audio. Two pretrained models, XLS-R for speech and BEATs for environmental sound, are used to extract complementary contextual representations. A Matching Head is introduced to model representation differences through statistical normalization and representation interaction, enabling estimation of the original class. In parallel, multi-head cross-attention enables effective information exchange between speech and environmental components. The refined representations are processed with residual connections and layer normalization, and passed to an AASIST classifier to predict speech-based and environment-based spoofing probabilities. The model outputs original, speech, and environment predictions. On the test set, the proposed system achieves an F1-score of 70.20% and an environmental EER of 16.54%, outperforming the baseline system.