@TrustAI
  • Stars
    star
    319
  • Global Org. Rank 31,185 (Top 10 %)
  • Registered over 6 years ago
  • Most used languages
    Python
    78.6 %
    MATLAB
    14.3 %
    TeX
    7.1 %

Top repositories

1

DeepConcolic

Concolic Testing for Deep Neural Networks
Python
117
star
2

Literature-on-DNN-Verification-and-Testing

TeX
46
star
3

DeepGO

Reachability Analysis of Deep Neural Networks with Provable Guarantees
MATLAB
34
star
4

DeepGame

A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees
Python
19
star
5

DeepCover

Testing Deep Neural Networks
Python
15
star
6

DeepSAVA

DeepSAVA: Sparse Adversarial Video Attacks with Spatial Transformations - BMVC 2021 & Neural Networks (2023)
Python
9
star
7

AdversarialDriving

Falsification Tool for Assessing Robustness of End-to-End Autonomous Driving Systems
Python
7
star
8

ODE4RobustViT

Understanding Adversarial Robustness of Vision Transformers via Cauchy Problem - ECML 2022 & Software Impacts (2023)
Python
5
star
9

GeoRobust

Towards Verifying the Geometric Robustness of Large-scale Neural Networks - AAAI 2023
Python
5
star
10

DeepNNC

Reachability Analysis of Neural Network Control Systems - AAAI 2023
4
star
11

DIMBA

DIMBA: Discretely Masked Black-Box Attack in Single Object Tracking - Machine Learning Journal (2022)
Python
4
star
12

FAAL

Towards Fairness-Aware Adversarial Learning - CVPR 2024
3
star
13

SCALA

An Efficient Word-level Black-box Adversarial Attack Against Textual Models - IJCNN'23
3
star
14

CertifyCMARL

Certified Policy Smoothing for Cooperative Multi-Agent Reinforcement Learning - AAAI 2023
Python
2
star
15

DeepQuant

Quantifying the Robustness of Deep Neural Networks - Complex & Intelligent Systems
MATLAB
2
star
16

DEAT

Dynamic Efficient Adversarial Training Guided by Gradient Magnitude - TEA@NeurIPS 2022
Python
2
star
17

TextVerifer

Towards Local Robustness Verification for Textual Classifiers with Certifiable Guarantees in Hamming Space - ACL 2023
2
star
18

NRAT

NRAT: Towards Adversarial Training with Inherent Label Noise - Machine Learning Journal (2023)
Python
1
star