Search Results for author:
Found 10 papers, 6 papers with code
Improving Membership Inference in ASR Model Auditing with Perturbed Loss Features
Membership Inference (MI) poses a substantial privacy threat to the training data of Automatic Speech Recognition (ASR) systems, while also offering an opportunity to audit these models with regard to user data.
LoFT: Local Proxy Fine-tuning For Improving Transferability Of Adversarial Attacks Against Large Language Model
We hypothesize that for attacks to be transferrable, it is sufficient if the proxy can approximate the target model in the neighborhood of the harmful query.
There is more than one kind of robustness: Fooling Whisper with adversarial examples
Whisper is a recent Automatic Speech Recognition (ASR) model displaying impressive robustness to both out-of-distribution inputs and random noise.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Watch What You Pretrain For: Targeted, Transferable Adversarial Examples on Self-Supervised Speech Recognition models
To exploit ASR models in real-world, black-box settings, an adversary can leverage the transferability property, i. e. that an adversarial sample produced for a proxy ASR can also fool a different remote ASR.
How many perturbations break this model? Evaluating robustness beyond adversarial accuracy
Finally, with sparsity we can measure increases in robustness that do not affect accuracy: we show for example that data augmentation can by itself increase adversarial robustness, without using adversarial training.
Recent improvements of ASR models in the face of adversarial attacks
Like many other tasks involving neural networks, Speech Recognition models are vulnerable to adversarial attacks.
Sequential Randomized Smoothing for Adversarially Robust Speech Recognition
We apply adaptive versions of state-of-the-art attacks, such as the Imperceptible ASR attack, to our model, and show that our strongest defense is robust to all attacks that use inaudible noise, and can only be broken with very high distortion.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Exploiting Non-Linear Redundancy for Neural Model Compression
Deploying deep learning models, comprising of non-linear combination of millions, even billions, of parameters is challenging given the memory, power and compute constraints of the real world.
In-training Matrix Factorization for Parameter-frugal Neural Machine Translation
In this paper, we propose the use of in-training matrix factorization to reduce the model size for neural machine translation.
Retrieval-Based Neural Code Generation
In models to generate program source code from natural language, representing this code in a tree structure has been a common approach.
