Search Results for author: Atul Prakash

Found 30 papers, 7 papers with code

Adaptive Skeleton Graph Decoding

no code implementations19 Feb 2024 Shuowei Jin, Yongji Wu, Haizhong Zheng, Qingzhao Zhang, Matthew Lentz, Z. Morley Mao, Atul Prakash, Feng Qian, Danyang Zhuo

Large language models (LLMs) have seen significant adoption for natural language tasks, owing their success to massive numbers of model parameters (e. g., 70B+); however, LLM inference incurs significant computation and memory costs.

Learn To be Efficient: Build Structured Sparsity in Large Language Models

no code implementations9 Feb 2024 Haizhong Zheng, Xiaoyan Bai, Beidi Chen, Fan Lai, Atul Prakash

The emergence of activation sparsity in LLMs provides a natural approach to reduce this cost by involving only parts of the parameters for inference.

Text Generation

Leveraging Hierarchical Feature Sharing for Efficient Dataset Condensation

no code implementations11 Oct 2023 Haizhong Zheng, Jiachen Sun, Shutong Wu, Bhavya Kailkhura, Zhuoqing Mao, Chaowei Xiao, Atul Prakash

In this paper, we recognize that images share common features in a hierarchical way due to the inherent hierarchical structure of the classification system, which is overlooked by current data parameterization methods.

Dataset Condensation

Theoretically Principled Trade-off for Stateful Defenses against Query-Based Black-Box Attacks

no code implementations30 Jul 2023 Ashish Hooda, Neal Mangaokar, Ryan Feng, Kassem Fawaz, Somesh Jha, Atul Prakash

This work aims to address this gap by offering a theoretical characterization of the trade-off between detection and false positive rates for stateful defenses.

CALICO: Self-Supervised Camera-LiDAR Contrastive Pre-training for BEV Perception

no code implementations1 Jun 2023 Jiachen Sun, Haizhong Zheng, Qingzhao Zhang, Atul Prakash, Z. Morley Mao, Chaowei Xiao

CALICO's efficacy is substantiated by extensive evaluations on 3D object detection and BEV map segmentation tasks, where it delivers significant performance improvements.

3D Object Detection Autonomous Driving +3

Stateful Defenses for Machine Learning Models Are Not Yet Secure Against Black-box Attacks

1 code implementation11 Mar 2023 Ryan Feng, Ashish Hooda, Neal Mangaokar, Kassem Fawaz, Somesh Jha, Atul Prakash

Such stateful defenses aim to defend against black-box attacks by tracking the query history and detecting and rejecting queries that are "similar" and thus preventing black-box attacks from finding useful gradients and making progress towards finding adversarial attacks within a reasonable query budget.

Coverage-centric Coreset Selection for High Pruning Rates

1 code implementation28 Oct 2022 Haizhong Zheng, Rui Liu, Fan Lai, Atul Prakash

We then propose a novel one-shot coreset selection method, Coverage-centric Coreset Selection (CCS), that jointly considers overall data coverage upon a distribution as well as the importance of each example.

Vocal Bursts Intensity Prediction

Constraining the Attack Space of Machine Learning Models with Distribution Clamping Preprocessing

no code implementations18 May 2022 Ryan Feng, Somesh Jha, Atul Prakash

Preprocessing and outlier detection techniques have both been applied to neural networks to increase robustness with varying degrees of success.

BIG-bench Machine Learning object-detection +2

Concept-based Explanations for Out-Of-Distribution Detectors

1 code implementation4 Mar 2022 Jihye Choi, Jayaram Raghuram, Ryan Feng, Jiefeng Chen, Somesh Jha, Atul Prakash

Based on these metrics, we propose an unsupervised framework for learning a set of concepts that satisfy the desired properties of high detection completeness and concept separability, and demonstrate its effectiveness in providing concept-based explanations for diverse off-the-shelf OOD detectors.

Out of Distribution (OOD) Detection

D4: Detection of Adversarial Diffusion Deepfakes Using Disjoint Ensembles

no code implementations11 Feb 2022 Ashish Hooda, Neal Mangaokar, Ryan Feng, Kassem Fawaz, Somesh Jha, Atul Prakash

D4 uses an ensemble of models over disjoint subsets of the frequency spectrum to significantly improve adversarial robustness.

Adversarial Robustness DeepFake Detection +1

Sneakoscope: Revisiting Unsupervised Out-of-Distribution Detection

no code implementations29 Sep 2021 Tianji Cong, Atul Prakash

The problem of detecting out-of-distribution (OOD) examples in neural networks has been widely studied in the literature, with state-of-the-art techniques being supervised in that they require fine-tuning on OOD data to achieve high-quality OOD detection.

Out-of-Distribution Detection Out of Distribution (OOD) Detection

Content-Adaptive Pixel Discretization to Improve Model Robustness

no code implementations3 Dec 2020 Ryan Feng, Wu-chi Feng, Atul Prakash

We first formally prove that adaptive codebooks can provide stronger robustness guarantees than fixed codebooks as a preprocessing defense on some datasets.

Understanding and Diagnosing Vulnerability under Adversarial Attacks

no code implementations17 Jul 2020 Haizhong Zheng, Ziqi Zhang, Honglak Lee, Atul Prakash

Moreover, we design the first diagnostic method to quantify the vulnerability contributed by each layer, which can be used to identify vulnerable parts of model architectures.

Classification General Classification

Towards Robustness against Unsuspicious Adversarial Examples

no code implementations8 May 2020 Liang Tong, Minzhe Guo, Atul Prakash, Yevgeniy Vorobeychik

We then experimentally demonstrate that our attacks indeed do not significantly change perceptual salience of the background, but are highly effective against classifiers robust to conventional attacks.

GRAPHITE: Generating Automatic Physical Examples for Machine-Learning Attacks on Computer Vision Systems

1 code implementation17 Feb 2020 Ryan Feng, Neal Mangaokar, Jiefeng Chen, Earlence Fernandes, Somesh Jha, Atul Prakash

We address three key requirements for practical attacks for the real-world: 1) automatically constraining the size and shape of the attack so it can be applied with stickers, 2) transform-robustness, i. e., robustness of a attack to environmental physical variations such as viewpoint and lighting changes, and 3) supporting attacks in not only white-box, but also black-box hard-label scenarios, so that the adversary can attack proprietary models.

BIG-bench Machine Learning General Classification +1

Efficient Adversarial Training with Transferable Adversarial Examples

2 code implementations CVPR 2020 Haizhong Zheng, Ziqi Zhang, Juncheng Gu, Honglak Lee, Atul Prakash

Adversarial training is an effective defense method to protect classification models against adversarial attacks.

Can Attention Masks Improve Adversarial Robustness?

no code implementations27 Nov 2019 Pratik Vaishnavi, Tianji Cong, Kevin Eykholt, Atul Prakash, Amir Rahmati

Focusing on the observation that discrete pixelization in MNIST makes the background completely black and foreground completely white, we hypothesize that the important property for increasing robustness is the elimination of image background using attention masks before classifying an object.

Adversarial Robustness

Towards Model-Agnostic Adversarial Defenses using Adversarially Trained Autoencoders

no code implementations12 Sep 2019 Pratik Vaishnavi, Kevin Eykholt, Atul Prakash, Amir Rahmati

Numerous techniques have been proposed to harden machine learning algorithms and mitigate the effect of adversarial attacks.

Adversarial Defense Adversarial Robustness +1

Analyzing the Interpretability Robustness of Self-Explaining Models

no code implementations27 May 2019 Haizhong Zheng, Earlence Fernandes, Atul Prakash

Recently, interpretable models called self-explaining models (SEMs) have been proposed with the goal of providing interpretability robustness.

Robust Classification using Robust Feature Augmentation

no code implementations26 May 2019 Kevin Eykholt, Swati Gupta, Atul Prakash, Amir Rahmati, Pratik Vaishnavi, Haizhong Zheng

Existing deep neural networks, say for image classification, have been shown to be vulnerable to adversarial images that can cause a DNN misclassification, without any perceptible change to an image.

Binarization Classification +3

Designing Adversarially Resilient Classifiers using Resilient Feature Engineering

no code implementations17 Dec 2018 Kevin Eykholt, Atul Prakash

We provide a methodology, resilient feature engineering, for creating adversarially resilient classifiers.

Feature Engineering General Classification

Physical Adversarial Examples for Object Detectors

no code implementations20 Jul 2018 Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Florian Tramer, Atul Prakash, Tadayoshi Kohno, Dawn Song

In this work, we extend physical attacks to more challenging object detection models, a broader class of deep learning algorithms widely used to detect and label multiple objects within a scene.

Object object-detection +1

Robust Physical-World Attacks on Deep Learning Visual Classification

no code implementations CVPR 2018 Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, Dawn Song

Recent studies show that the state-of-the-art deep neural networks (DNNs) are vulnerable to adversarial examples, resulting from small-magnitude perturbations added to the input.

Classification General Classification +1

Tyche: Risk-Based Permissions for Smart Home Platforms

no code implementations14 Jan 2018 Amir Rahmati, Earlence Fernandes, Kevin Eykholt, Atul Prakash

When using risk-based permissions, device operations are grouped into units of similar risk, and users grant apps access to devices at that risk-based granularity.

Cryptography and Security

Note on Attacking Object Detectors with Adversarial Stickers

no code implementations21 Dec 2017 Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Dawn Song, Tadayoshi Kohno, Amir Rahmati, Atul Prakash, Florian Tramer

Given the fact that state-of-the-art objection detection algorithms are harder to be fooled by the same set of adversarial examples, here we show that these detectors can also be attacked by physical adversarial examples.

Object Test

Robust Physical-World Attacks on Deep Learning Models

1 code implementation27 Jul 2017 Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, Dawn Song

We propose a general attack algorithm, Robust Physical Perturbations (RP2), to generate robust visual adversarial perturbations under different physical conditions.

Test

Internet of Things Security Research: A Rehash of Old Ideas or New Intellectual Challenges?

no code implementations23 May 2017 Earlence Fernandes, Amir Rahmati, Kevin Eykholt, Atul Prakash

The Internet of Things (IoT) is a new computing paradigm that spans wearable devices, homes, hospitals, cities, transportation, and critical infrastructure.

Cryptography and Security

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