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Found 9 papers, 5 papers with code
Fast Adversarial Attacks on Language Models In One GPU Minute
Through human evaluations, we find that our untargeted attack causes Vicuna-7B-v1. 5 to produce ~15% more incorrect outputs when compared to LM outputs in the absence of our attack.
Robustness of AI-Image Detectors: Fundamental Limits and Practical Attacks
Moreover, we show that watermarking methods are vulnerable to spoofing attacks where the attacker aims to have real images identified as watermarked ones, damaging the reputation of the developers.
Provable Robustness for Streaming Models with a Sliding Window
Robustness certificates based on the assumption of independent input samples are not directly applicable in such scenarios.
Can AI-Generated Text be Reliably Detected?
In particular, we develop a recursive paraphrasing attack to apply on AI text, which can break a whole range of detectors, including the ones using the watermarking schemes as well as neural network-based detectors, zero-shot classifiers, and retrieval-based detectors.
CUDA: Convolution-based Unlearnable Datasets
Here, ERM on the clean training data achieves a clean test accuracy of 80. 66$\%$.
OSSuM: A Gradient-Free Approach For Pruning Neural Networks At Initialization
Recent works attempt to prune neural networks at initialization to design sparse networks that can be trained efficiently.
Statistical Measures For Defining Curriculum Scoring Function
Curriculum learning is a training strategy that sorts the training examples by some measure of their difficulty and gradually exposes them to the learner to improve the network performance.
A Simple Approach To Define Curricula For Training Neural Networks
Curriculum learning is a training strategy that sorts the training examples by their difficulty and gradually exposes them to the learner.
Shallow RNN: Accurate Time-series Classification on Resource Constrained Devices
The second layer consumes the output of the first layer using a second RNN thus capturing long dependencies.
