Jan Rybarczyk | Search arXiv

arXiv2026

DFD-Lab: A Modular Audio-Visual Deepfake Detection Pipeline

Comparing audio-visual deepfake detectors requires coordinating dataset adaptation, temporal input representation, model interfaces and experimental conditions. We present DFD-Lab, a modular pipeline that separates these responsibilities while supporting shared training and evaluation workflows. We integrate three implementations: Xception-based maximum-logit fusion, ResNet with temporal LSTM fusion, and our AVFF reimplementation. Experiments cover external testing, degradation-based training augmentation and evaluation-time corruption. On a filtered subset of Deepfake-Eval-2024, models trained on FakeAVCeleb attain baseline AUROC values of 0.504, 0.538 and 0.458. JPEG50 training augmentation raises these to 0.691, 0.605 and 0.570, respectively, while all three accuracies decrease. These results illustrate why training interventions, evaluation corruptions and metric-dependent outcomes should remain distinct within a common pipeline. The contribution is the integration of audio-visual processing, interchangeable detectors and configurable experimental workflows, supported by empirical case studies. The findings highlight the challenge of cross-dataset detection and the complementary information provided by ranking and classification metrics.

cs.CV↗

arXiv2023

Adapt & Align: Continual Learning with Generative Models Latent Space Alignment

In this work, we introduce Adapt & Align, a method for continual learning of neural networks by aligning latent representations in generative models. Neural Networks suffer from abrupt loss in performance when retrained with additional training data from different distributions. At the same time, training with additional data without access to the previous examples rarely improves the model's performance. In this work, we propose a new method that mitigates those problems by employing generative models and splitting the process of their update into two parts. In the first one, we train a local generative model using only data from a new task. In the second phase, we consolidate latent representations from the local model with a global one that encodes knowledge of all past experiences. We introduce our approach with Variational Auteoncoders and Generative Adversarial Networks. Moreover, we show how we can use those generative models as a general method for continual knowledge consolidation that can be used in downstream tasks such as classification.

cs.LG↗