DeepJudge: A Testing Framework for Copyright Protection of Deep Learning Models
Deep learning (DL) models have become one of the most valuable assets in modern society, and those most complex ones require millions of dollars for the model development. As a result, unauthorized duplication or reproduction of DL models can lead to copyright infringement and cause huge economic losses to model owners. In this work, we present DeepJudge, a testing framework for DL copyright protection. DeepJudge quantitatively tests the similarities between two DL models: a victim model and a suspect model. It leverages a diverse set of testing metrics and efficient test case generation algorithms to produce a chain of supporting evidence to help determine whether a suspect model is a copy of the victim model. Our experiments confirm the effectiveness of DeepJudge under typical model copyright infringement scenarios. The tool has been made publicly available at https://github.com/Testing4AI/DeepJudge. A demo video can be found at https://www.youtube.com/watch?v=MhbIkd0OQgU.
Thu 18 MayDisplayed time zone: Hobart change
11:00 - 12:30 | AI testing 1Technical Track / DEMO - Demonstrations / Journal-First Papers at Meeting Room 102 Chair(s): Matthew B Dwyer University of Virginia | ||
11:00 15mTalk | When and Why Test Generators for Deep Learning Produce Invalid Inputs: an Empirical Study Technical Track Pre-print | ||
11:15 15mTalk | Fuzzing Automatic Differentiation in Deep-Learning Libraries Technical Track Chenyuan Yang University of Illinois at Urbana-Champaign, Yinlin Deng University of Illinois at Urbana-Champaign, Jiayi Yao The Chinese University of Hong Kong, Shenzhen, Yuxing Tu Huazhong University of Science and Technology, Hanchi Li University of Science and Technology of China, Lingming Zhang University of Illinois at Urbana-Champaign | ||
11:30 15mTalk | Lightweight Approaches to DNN Regression Error Reduction: An Uncertainty Alignment Perspective Technical Track Zenan Li Nanjing University, China, Maorun Zhang Nanjing University, China, Jingwei Xu , Yuan Yao Nanjing University, Chun Cao Nanjing University, Taolue Chen Birkbeck University of London, Xiaoxing Ma Nanjing University, Jian Lu Nanjing University Pre-print | ||
11:45 7mTalk | DeepJudge: A Testing Framework for Copyright Protection of Deep Learning Models DEMO - Demonstrations Jialuo Chen Zhejiang University, Youcheng Sun The University of Manchester, Jingyi Wang Zhejiang University, Peng Cheng Zhejiang University, Xingjun Ma Deakin University | ||
11:52 7mTalk | DeepCrime: from Real Faults to Mutation Testing Tool for Deep Learning DEMO - Demonstrations | ||
12:00 7mTalk | DiverGet: a Search-Based Software Testing approach for Deep Neural Network Quantization assessment Journal-First Papers Ahmed Haj Yahmed École Polytechnique de Montréal, Houssem Ben Braiek École Polytechnique de Montréal, Foutse Khomh Polytechnique Montréal, Sonia Bouzidi National Institute of Applied Science and Technology, Rania Zaatour Potsdam Institute for Climate Impact Research | ||
12:07 15mTalk | Revisiting Neuron Coverage for DNN Testing: A Layer-Wise and Distribution-Aware Criterion Technical Track Yuanyuan Yuan The Hong Kong University of Science and Technology, Qi Pang HKUST, Shuai Wang Hong Kong University of Science and Technology | ||