Search Results for author:
Found 31 papers, 17 papers with code
RAFT: Realistic Attacks to Fool Text Detectors
We also show that examples generated by RAFT can be used to train adversarially robust detectors.
Learning to Rewrite: Generalized LLM-Generated Text Detection
Large language models (LLMs) can be abused at scale to create non-factual content and spread disinformation.
Turns Out I'm Not Real: Towards Robust Detection of AI-Generated Videos
In this paper, we propose a novel framework for detecting videos synthesized from multiple state-of-the-art (SOTA) generative models, such as Stable Video Diffusion.
ImageNet-D: Benchmarking Neural Network Robustness on Diffusion Synthetic Object
In this work, we introduce generative model as a data source for synthesizing hard images that benchmark deep models' robustness.
SelfIE: Self-Interpretation of Large Language Model Embeddings
How do large language models (LLMs) obtain their answers?
Raidar: geneRative AI Detection viA Rewriting
We find that large language models (LLMs) are more likely to modify human-written text than AI-generated text when tasked with rewriting.
Robustifying Language Models with Test-Time Adaptation
Instead, we propose to make the language models robust at test time.
Interpreting and Controlling Vision Foundation Models via Text Explanations
Large-scale pre-trained vision foundation models, such as CLIP, have become de facto backbones for various vision tasks.
Towards Causal Deep Learning for Vulnerability Detection
Our results show that CausalVul consistently improved the model accuracy, robustness and OOD performance for all the state-of-the-art models and datasets we experimented.
Monitoring and Adapting ML Models on Mobile Devices
ML models are increasingly being pushed to mobile devices, for low-latency inference and offline operation.
Test-time Detection and Repair of Adversarial Samples via Masked Autoencoder
Test-time defenses solve these issues but most existing test-time defenses require adapting the model weights, therefore they do not work on frozen models and complicate model memory management.
What You Can Reconstruct From a Shadow
Experiments and visualizations show that the method is able to generate multiple possible solutions that are consistent with the observation of the shadow.
Understanding Zero-Shot Adversarial Robustness for Large-Scale Models
We apply this training loss to two adaption methods, model finetuning and visual prompt tuning.
Adversarially Robust Video Perception by Seeing Motion
Even under adaptive attacks where the adversary knows our defense, our algorithm is still effective.
Doubly Right Object Recognition: A Why Prompt for Visual Rationales
We propose a ``doubly right'' object recognition benchmark, where the metric requires the model to simultaneously produce both the right labels as well as the right rationales.
Robust Perception through Equivariance
In this paper, we introduce a framework that uses the dense intrinsic constraints in natural images to robustify inference.
1st ICLR International Workshop on Privacy, Accountability, Interpretability, Robustness, Reasoning on Structured Data (PAIR^2Struct)
Recent years have seen advances on principles and guidance relating to accountable and ethical use of artificial intelligence (AI) spring up around the globe.
Shadows Shed Light on 3D Objects
Experiments and visualizations show that the method is able to generate multiple possible solutions that are consistent with the observation of the shadow.
Landscape Learning for Neural Network Inversion
Many machine learning methods operate by inverting a neural network at inference time, which has become a popular technique for solving inverse problems in computer vision, robotics, and graphics.
Causal Transportability for Visual Recognition
Visual representations underlie object recognition tasks, but they often contain both robust and non-robust features.
A Tale of Two Models: Constructing Evasive Attacks on Edge Models
In this paper, we introduce a new evasive attack, DIVA, that exploits these differences in edge adaptation, by adding adversarial noise to input data that maximizes the output difference between the original and adapted model.
Using Multiple Self-Supervised Tasks Improves Model Robustness
In this paper, we propose a novel defense that can dynamically adapt the input using the intrinsic structure from multiple self-supervised tasks.
Real-Time Neural Voice Camouflage
Automatic speech recognition systems have created exciting possibilities for applications, however they also enable opportunities for systematic eavesdropping.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Discrete Representations Strengthen Vision Transformer Robustness
Vision Transformer (ViT) is emerging as the state-of-the-art architecture for image recognition.
Ranked #3 on
Domain Generalization
on Stylized-ImageNet
Adversarial Attacks are Reversible with Natural Supervision
We find that images contain intrinsic structure that enables the reversal of many adversarial attacks.
Generative Interventions for Causal Learning
We introduce a framework for learning robust visual representations that generalize to new viewpoints, backgrounds, and scene contexts.
Ranked #44 on
Image Classification
on ObjectNet
(using extra training data)
Multitask Learning Strengthens Adversarial Robustness
Although deep networks achieve strong accuracy on a range of computer vision benchmarks, they remain vulnerable to adversarial attacks, where imperceptible input perturbations fool the network.
Live Trojan Attacks on Deep Neural Networks
We propose a live attack on deep learning systems that patches model parameters in memory to achieve predefined malicious behavior on a certain set of inputs.
AdvSPADE: Realistic Unrestricted Attacks for Semantic Segmentation
Due to the inherent robustness of segmentation models, traditional norm-bounded attack methods show limited effect on such type of models.
Metric Learning for Adversarial Robustness
Deep networks are well-known to be fragile to adversarial attacks.
Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health Profiling
We consider the problem of inferring the values of an arbitrary set of variables (e. g., risk of diseases) given other observed variables (e. g., symptoms and diagnosed diseases) and high-dimensional signals (e. g., MRI images or EEG).
