Search Results for author: Junfeng Yang

Found 19 papers, 15 papers with code

Causal Transportability for Visual Recognition

1 code implementation26 Apr 2022 Chengzhi Mao, Kevin Xia, James Wang, Hao Wang, Junfeng Yang, Elias Bareinboim, Carl Vondrick

Visual representations underlie object recognition tasks, but they often contain both robust and non-robust features.

Image Classification Object Recognition +1

A Tale of Two Models: Constructing Evasive Attacks on Edge Models

1 code implementation22 Apr 2022 Wei Hao, Aahil Awatramani, Jiayang Hu, Chengzhi Mao, Pin-Chun Chen, Eyal Cidon, Asaf Cidon, Junfeng Yang

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.

Quantization

Using Multiple Self-Supervised Tasks Improves Model Robustness

1 code implementation7 Apr 2022 Matthew Lawhon, Chengzhi Mao, Junfeng Yang

In this paper, we propose a novel defense that can dynamically adapt the input using the intrinsic structure from multiple self-supervised tasks.

Adversarial Attacks are Reversible with Natural Supervision

1 code implementation ICCV 2021 Chengzhi Mao, Mia Chiquier, Hao Wang, Junfeng Yang, Carl Vondrick

We find that images contain intrinsic structure that enables the reversal of many adversarial attacks.

BPF for storage: an exokernel-inspired approach

1 code implementation25 Feb 2021 Yu Jian Wu, Hongyi Wang, Yuhong Zhong, Asaf Cidon, Ryan Stutsman, Amy Tai, Junfeng Yang

The overhead of the kernel storage path accounts for half of the access latency for new NVMe storage devices.

Operating Systems Databases

Generative Interventions for Causal Learning

1 code implementation CVPR 2021 Chengzhi Mao, Augustine Cha, Amogh Gupta, Hao Wang, Junfeng Yang, Carl Vondrick

We introduce a framework for learning robust visual representations that generalize to new viewpoints, backgrounds, and scene contexts.

Out-of-Distribution Generalization

Trex: Learning Execution Semantics from Micro-Traces for Binary Similarity

no code implementations16 Dec 2020 Kexin Pei, Zhou Xuan, Junfeng Yang, Suman Jana, Baishakhi Ray

We thus train the model to learn execution semantics from the functions' micro-traces, without any manual labeling effort.

Transfer Learning Vulnerability Detection

What Does CNN Shift Invariance Look Like? A Visualization Study

1 code implementation9 Nov 2020 Jake Lee, Junfeng Yang, Zhangyang Wang

We present the results of three experiments comparing representations of millions of images with exhaustively shifted objects, examining both local invariance (within a few pixels) and global invariance (across the image frame).

Frame

XDA: Accurate, Robust Disassembly with Transfer Learning

1 code implementation2 Oct 2020 Kexin Pei, Jonas Guan, David Williams-King, Junfeng Yang, Suman Jana

We present XDA, a transfer-learning-based disassembly framework that learns different contextual dependencies present in machine code and transfers this knowledge for accurate and robust disassembly.

Language Modelling Masked Language Modeling +2

Multitask Learning Strengthens Adversarial Robustness

1 code implementation ECCV 2020 Chengzhi Mao, Amogh Gupta, Vikram Nitin, Baishakhi Ray, Shuran Song, Junfeng Yang, Carl Vondrick

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.

Adversarial Defense Adversarial Robustness

Live Trojan Attacks on Deep Neural Networks

1 code implementation22 Apr 2020 Robby Costales, Chengzhi Mao, Raphael Norwitz, Bryan Kim, Junfeng Yang

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

no code implementations6 Oct 2019 Guangyu Shen, Chengzhi Mao, Junfeng Yang, Baishakhi Ray

Due to the inherent robustness of segmentation models, traditional norm-bounded attack methods show limited effect on such type of models.

Adversarial Attack Semantic Segmentation

Efficient Formal Safety Analysis of Neural Networks

2 code implementations NeurIPS 2018 Shiqi Wang, Kexin Pei, Justin Whitehouse, Junfeng Yang, Suman Jana

Our approach can check different safety properties and find concrete counterexamples for networks that are 10$\times$ larger than the ones supported by existing analysis techniques.

Adversarial Attack Adversarial Defense +2

NEUZZ: Efficient Fuzzing with Neural Program Smoothing

1 code implementation15 Jul 2018 Dongdong She, Kexin Pei, Dave Epstein, Junfeng Yang, Baishakhi Ray, Suman Jana

However, even state-of-the-art fuzzers are not very efficient at finding hard-to-trigger software bugs.

Formal Security Analysis of Neural Networks using Symbolic Intervals

3 code implementations28 Apr 2018 Shiqi Wang, Kexin Pei, Justin Whitehouse, Junfeng Yang, Suman Jana

In this paper, we present a new direction for formally checking security properties of DNNs without using SMT solvers.

Autonomous Vehicles

Towards Practical Verification of Machine Learning: The Case of Computer Vision Systems

no code implementations5 Dec 2017 Kexin Pei, Yinzhi Cao, Junfeng Yang, Suman Jana

Finally, we show that retraining using the safety violations detected by VeriVis can reduce the average number of violations up to 60. 2%.

Medical Diagnosis

PanNet: A Deep Network Architecture for Pan-Sharpening

no code implementations ICCV 2017 Junfeng Yang, Xueyang Fu, Yuwen Hu, Yue Huang, Xinghao Ding, John Paisley

We incorporate domain-specific knowledge to design our PanNet architecture by focusing on the two aims of the pan-sharpening problem: spectral and spatial preservation.

DeepXplore: Automated Whitebox Testing of Deep Learning Systems

3 code implementations18 May 2017 Kexin Pei, Yinzhi Cao, Junfeng Yang, Suman Jana

First, we introduce neuron coverage for systematically measuring the parts of a DL system exercised by test inputs.

Malware Detection Self-Driving Cars

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