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Modern deepfake techniques produce highly realistic false media content with the potential for spreading harmful information, including fake news and incitements to violence. Deepfake detection methods aim to identify and counteract such content by employing machine learning algorithms, focusing mainly on detecting the presence of manipulation using spatial and temporal features. These methods often utilize Foundation Models trained on extensive unlabeled data through self-supervised approaches. This work extends previous research on deepfake detection, focusing on the effectiveness of these models while also considering biases, particularly concerning age, gender, and ethnicity, for ethical analysis. Experiments with DINOv2, a novel Vision Transformer-based Foundation Model, trained using the diverse Deepfake Detection Challenge Dataset, which encompasses several lighting conditions, resolutions, and demographic attributes, demonstrated improved deepfake detection when combined with a CNN classifier, with minimal bias towards these demographic characteristics.

An Automatic Speech Recognition (ASR) System is a software tool that converts a speech audio waveform into its corresponding text transcription. ASR systems are usually built using Artificial Intelligence techniques, particularly Machine Learning algorithms like Deep Learning, to address the multi-faceted complexity and variability of human speech. This allows these systems to learn from extensive speech datasets, adapt to several languages and accents, and continuously improve their performance over time, making them each time more versatile and effective in their purpose of transcribing spoken language to text. Much in the same way, we argue that the noises commonly present in the different environments also need to be explicitly dealt with, and, when possible, modeled within specific datasets with proper training. Our motivation comes from the observation that noise removal techniques (commonly called denoising), are not always fully (and generically) efficient. For instance, noise degeneration due to communication interference, which is almost always present in radio transmissions, has peculiarities that a simple mathematical formulation cannot model. This work presents a modeling technique composed of an augmented dataset-building approach and a profile identifier that can be used to build ASRs for noisy environments that perform similarly to those used in noise-free environments. As a case study, we developed a specific ASR for the interference noise in radio transmissions with its specific dataset, while comparing our results with other state-of-the-art work. As a result, we report a Character Error Rate value of 0.3163 for the developed ASR under several different noise conditions.