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Similarity Choice and Negative Scaling in Supervised Contrastive Learning for Deepfake Audio Detection
Jaskirat Sudan, Hashim Ali, Surya Subramani +1
Supervised contrastive learning (SupCon) is widely used to shape representations, but has seen limited targeted study for audio deepfake detection. Existing work typically combines…
A SUPERB-Style Benchmark of Self-Supervised Speech Models for Audio Deepfake Detection
Hashim Ali, Nithin Sai Adupa, Surya Subramani +1
Self-supervised learning (SSL) has transformed speech processing, with benchmarks such as SUPERB establishing fair comparisons across diverse downstream tasks. Despite it's securit…
Multilingual Dataset Integration Strategies for Robust Audio Deepfake Detection: A SAFE Challenge System
Hashim Ali, Surya Subramani, Lekha Bollinani +3
The SAFE Challenge evaluates synthetic speech detection across three tasks: unmodified audio, processed audio with compression artifacts, and laundered audio designed to evade dete…
Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges
Hashim Ali, Surya Subramani, Raksha Varahamurthy +3
Recent advances in speech synthesis have introduced unprecedented challenges in maintaining voice authenticity, particularly concerning public figures who are frequent targets of i…
Augmentation through Laundering Attacks for Audio Spoof Detection
Hashim Ali, Surya Subramani, Hafiz Malik
Recent text-to-speech (TTS) developments have made voice cloning (VC) more realistic, affordable, and easily accessible. This has given rise to many potential abuses of this techno…
Is Audio Spoof Detection Robust to Laundering Attacks?
Hashim Ali, Surya Subramani, Shefali Sudhir +2
Voice-cloning (VC) systems have seen an exceptional increase in the realism of synthesized speech in recent years. The high quality of synthesized speech and the availability of lo…