  The state-of-the-art audio deepfake detectors leveraging deep neural networks
exhibit impressive recognition performance. Nonetheless, this advantage is
accompanied by a significant carbon footprint. This is mainly due to the use of
high-performance computing with accelerators and high training time. Studies
show that average deep NLP model produces around 626k lbs of
CO\textsubscript{2} which is equivalent to five times of average US car
emission at its lifetime. This is certainly a massive threat to the
environment. To tackle this challenge, this study presents a novel framework
for audio deepfake detection that can be seamlessly trained using standard CPU
resources. Our proposed framework utilizes off-the-shelve self-supervised
learning (SSL) based models which are pre-trained and available in public
repositories. In contrast to existing methods that fine-tune SSL models and
employ additional deep neural networks for downstream tasks, we exploit
classical machine learning algorithms such as logistic regression and shallow
neural networks using the SSL embeddings extracted using the pre-trained model.
Our approach shows competitive results compared to the commonly used
high-carbon footprint approaches. In experiments with the ASVspoof 2019 LA
dataset, we achieve a 0.90\% equal error rate (EER) with less than 1k trainable
model parameters. To encourage further research in this direction and support
reproducible results, the Python code will be made publicly accessible
following acceptance. Github: https://github.com/sahasubhajit/Speech-Spoofing-
