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https://github.com/openphilanthropy/unrestricted-adversarial-examples

Contest Proposal and infrastructure for the Unrestricted Adversarial Examples Challenge
https://github.com/openphilanthropy/unrestricted-adversarial-examples

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Contest Proposal and infrastructure for the Unrestricted Adversarial Examples Challenge

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README 

        
# Unrestricted Adversarial Examples Challenge [![Build Status](https://travis-ci.org/google/unrestricted-adversarial-examples.svg?branch=master)](https://travis-ci.org/google/unrestricted-adversarial-examples)



In the Unrestricted Adversarial Examples Challenge, attackers submit arbitrary adversarial inputs, and defenders are expected to assign low confidence to difficult inputs while retaining high confidence and accuracy on a clean, unambiguous test set. You can learn more about the motivation and structure of the contest in [our recent paper](https://drive.google.com/open?id=1T0yiu9LPv_Qh-qYhYFLj9dxjnkca8fkG)



This repository contains code for [the warm-up to the challenge](warmup.md), as well as [the public proposal for the contest](contest_proposal.md). We are currently accepting defenses for the warm-up. 



![image](https://user-images.githubusercontent.com/306655/44686400-f0b74800-aa02-11e8-8967-fa354244813f.png)



### Current Status (Updated April 2020)

The [latest submission by Chongli Qin et al](https://github.com/deepmind/deepmind-research/tree/master/unrestricted_advx) has claimed to solve the warm-up to the challenge. We are verifying the submission with our advisory board, and preparing to launch the full-fledged version of the contest.



### Leaderboard for the warm-up to the contest

We include three attacks in [the warm-up to the contest](warmup.md):



- 1000 Linfinity-ball adversarial examples generated by [SPSA](https://arxiv.org/pdf/1802.05666.pdf)

- 1000 spatial adversarial examples [(via grid search)](https://arxiv.org/abs/1712.02779)

- 100 L2-ball adversarial examples generated by the [Boundary attack](https://arxiv.org/abs/1712.04248)



The top few distinct models for each dataset are shown below.  You can see all submissions in [the full scoreboard](scoreboard.md). 



#### Two-Class MNIST dataset

| Defense               | Submitted by  | Clean data | Spatial grid attack | SPSA attack | Boundary attack |  Submission Date | Open Source |

| --------------------- | ------------- | ------------ |------------ |--------------- |--------------- | --------------- | --------------- |

| [MadryPGD LeNet Baseline](unrestricted-advex/unrestricted_advex/mnist_baselines)  |  Google Brain |    100.0%    |      0%    |     19.6%   |     0%     |  Sept 14th, 2018 | Yes |

| [Undefended LeNet Baseline](unrestricted-advex/unrestricted_advex/mnist_baselines)   |  Google Brain   |    100.0%    |     0%    |     0%    |     0%     |  Sept 14th, 2018 | Yes |



All percentages above correspond to the model's accuracy at 80% coverage.



#### Bird or Bicycle dataset

| Defense               | Submitted by  | Clean data | Common corruptions | Spatial grid attack | SPSA attack | Boundary attack |  Submission Date | Open Source |

| --------------------- | ------------- | ------------| ------------ |--------------- |-------- | ------- | --------------- | --------------- |

| [LLR_ADV_TRAIN](https://github.com/deepmind/deepmind-research/tree/master/unrestricted_advx) | Chongli Qin & Jonathan Uesato | 100.0% | 100.0% | 100.0% | 100.0% | 100.0% | Dec 14th, 2019 | Yes|

| [TRADESv2](https://github.com/xincoder/google_attack) |Hongyang Zhang (CMU) & Xin Li (Lehigh Univ.)|100.0%|100.0%|99.5%|100.0%|95.0%|Jan 17th, 2019 | No |

| [Keras ResNet 
(trained on ImageNet)](examples/undefended_keras_resnet)   |  Google Brain   |    100.0%    |    99.2%    |  92.2%    |     1.6%    |     4.0%     |  Sept 29th, 2018 | Yes |

| [Pytorch ResNet 
(trained on bird-or-bicycle extras)](examples/undefended_pytorch_resnet)  |  Google Brain |    98.8%    |   74.6%    |      49.5%    |   2.5%   |     8.0%     |  Oct 1st, 2018 | Yes |



All percentages above correspond to the model's accuracy at 80% coverage.



### Submitting a defense for the warm-up



The [warm-up before the contest](warmup.md) is currently underway and is accepting submissions. If you have additional questions, feel free to [submit a new GitHub issue](https://github.com/google/unrestricted-adversarial-examples/issues/new) with the "question" tag and we will respond shortly.



## The contest



The contest phase will begin after the warm-up attacks have been conclusively solved. We have published the [contest proposal](https://github.com/google/unrestricted-adversarial-examples/blob/master/contest_proposal.md) and are soliciting feedback from the community.



## Paper

You can learn more about the motivation and structure of the contest in our [recent paper](https://arxiv.org/abs/1809.08352):



**Unrestricted Adversarial Examples**


*Tom B. Brown, Nicholas Carlini, Chiyuan Zhang, Catherine Olsson, Paul Christiano and Ian Goodfellow*


[Arxiv preprint](https://arxiv.org/abs/1809.08352)



```

@article{unrestricted_advex_2018,

  title = {Unrestricted Adversarial Examples},

  author = {{Brown}, T.~B. and {Carlini}, N. and {Zhang}, C. and {Olsson}, C. and 

	  {Christiano}, P. and {Goodfellow}, I.},

  journal={arXiv preprint arXiv:1809.08352},

  year={2018}

}

```