Search Results for author:
Found 12 papers, 2 papers with code
Imbalance in Regression Datasets
For classification, the problem of class imbalance is well known and has been extensively studied.
Protecting Publicly Available Data With Machine Learning Shortcuts
Machine-learning (ML) shortcuts or spurious correlations are artifacts in datasets that lead to very good training and test performance but severely limit the model's generalization capability.
Complex-valued neural networks for voice anti-spoofing
Current anti-spoofing and audio deepfake detection systems use either magnitude spectrogram-based features (such as CQT or Melspectrograms) or raw audio processed through convolution or sinc-layers.
Shortcut Detection with Variational Autoencoders
For real-world applications of machine learning (ML), it is essential that models make predictions based on well-generalizing features rather than spurious correlations in the data.
Localized Shortcut Removal
This is often due to the existence of machine learning shortcuts - features in the data that are predictive but unrelated to the problem at hand.
Does Audio Deepfake Detection Generalize?
Current text-to-speech algorithms produce realistic fakes of human voices, making deepfake detection a much-needed area of research.
Attacker Attribution of Audio Deepfakes
This is despite the fact that attribution (who created which fake?)
Human Perception of Audio Deepfakes
The recent emergence of deepfakes has brought manipulated and generated content to the forefront of machine learning research.
Defending Against Adversarial Denial-of-Service Data Poisoning Attacks
Since many applications rely on untrusted training data, an attacker can easily craft malicious samples and inject them into the training dataset to degrade the performance of machine learning models.
Adversarial Vulnerability of Active Transfer Learning
In this paper, we share an intriguing observation: Namely, that the combination of these techniques is particularly susceptible to a new kind of data poisoning attack: By adding small adversarial noise on the input, it is possible to create a collision in the output space of the transfer learner.
Towards Resistant Audio Adversarial Examples
We devise an approach to mitigate this flaw and find that our method improves generation of adversarial examples with varying offsets.
