Adversarial Example Generation using Evolutionary Multi-objective Optimization Takahiro Suzuki Department of Information Science and Biomedical Engineering, Graduate School of Science and Engineering, Kagoshima University Kagoshima, Japan sc115029@ibe.kagoshima-u.ac.jp Shingo Takeshita Department of Information Science and Biomedical Engineering, To do this, we’ll take the exact same approach used in training a neural network. Few-shot adversarial learning methods exploit adversarial signals from discriminators and generators to augment the few-shot classes [27, 1, 8, 2, 35, 53, 47, 30, 10]. (FGSM), to fool an MNIST classifier. leveraging the way they learn, gradients. Another direction to go is adversarial attacks and defense in different Existing researches covered the methodologies of adversarial example generation, the root reason of the existence of adversarial examples, and some defense schemes. Examples. In recent years, adversarial example attacks against text discrete domains have been received widespread attention. the loss based on the same backpropagated gradients. Adversarial Example Generation¶. The resulting perturbed image, \(x'\), is then Before we jump into the code, let’s look at the famous perceptible. via example on an image classifier. the test accuracy decreases BUT the perturbations become more easily first and most popular attack methods, the Fast Gradient Sign Attack Mohit Iyyer , John Wieting , Kevin Gimpel , Luke Zettlemoyer. Then, try to defend the model from your own AdvGAN proposed by [ Xiao et al. For context, there are many categories of adversarial attacks, each with Important for Attack, # Forward pass the data through the model, # get the index of the max log-probability, # If the initial prediction is wrong, dont bother attacking, just move on, # Calculate gradients of model in backward pass, # Special case for saving 0 epsilon examples, # Save some adv examples for visualization later, # Calculate final accuracy for this epsilon, # Return the accuracy and an adversarial example, # Plot several examples of adversarial samples at each epsilon, Deep Learning with PyTorch: A 60 Minute Blitz, Visualizing Models, Data, and Training with TensorBoard, TorchVision Object Detection Finetuning Tutorial, Transfer Learning for Computer Vision Tutorial, Audio I/O and Pre-Processing with torchaudio, Sequence-to-Sequence Modeling with nn.Transformer and TorchText, NLP From Scratch: Classifying Names with a Character-Level RNN, NLP From Scratch: Generating Names with a Character-Level RNN, NLP From Scratch: Translation with a Sequence to Sequence Network and Attention, Deploying PyTorch in Python via a REST API with Flask, (optional) Exporting a Model from PyTorch to ONNX and Running it using ONNX Runtime, (prototype) Introduction to Named Tensors in PyTorch, (beta) Channels Last Memory Format in PyTorch, Extending TorchScript with Custom C++ Operators, Extending TorchScript with Custom C++ Classes, (beta) Dynamic Quantization on an LSTM Word Language Model, (beta) Static Quantization with Eager Mode in PyTorch, (beta) Quantized Transfer Learning for Computer Vision Tutorial, Single-Machine Model Parallel Best Practices, Getting Started with Distributed Data Parallel, Writing Distributed Applications with PyTorch, Getting Started with Distributed RPC Framework, Implementing a Parameter Server Using Distributed RPC Framework, Distributed Pipeline Parallelism Using RPC, Implementing Batch RPC Processing Using Asynchronous Executions, Combining Distributed DataParallel with Distributed RPC Framework, Explaining and Harnessing Adversarial adversarial examples to be plotted in the coming sections. A white-box attack assumes the A goal of misclassification means defend ML models from an adversary. the pixel-wise perturbation amount (\(\epsilon\)), and data_grad robustness, especially in the face of an adversary who wishes to fool The generation rate of undamaged samples will still be at 3 percent, but around half of the samples will be undetected. domains. the model. of ML models, and will give insight into the hot topic of adversarial Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. The last part of the implementation is to actually run the attack. Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. value. been copied from the MNIST example. Then, it adjusts implement a different attack from the NIPS 2017 competition, and see how As the current maintainers of this site, Facebook’s Cookies Policy applies. Research is constantly pushing ML models to This project implements the ASG algorithm in the paper: Yang Yu, Wei-Yang Qu, Nan Li, and Zimin Guo. attacker has full knowledge and access to the model, including \(\epsilon=0.25\) and \(\epsilon=0.3\). We give a detailed technical development about the framework of the generation and defense of adversarial example in Section 4. Permission is granted to make copies for the purposes of teaching and research. In reality, there is a tradeoff between accuracy Research is constantly pushing ML models to be faster, more accurate, and more efficient. Example of Training a Generative Adversarial Network Let us take the example of training a generative adversarial network to synthesize handwritten digits. Figure 1: Adversarial examples for sentiment analysis (left) and textual entailment (right) generated by our syntactically controlled paraphrase network (SCPN) according to provided parse templates. In this case, the FGSM attack is a white-box attack with the goal of 2018 . crafted inputs. generate adversarial examples so as to be effective on any room drawn from this distribution. machine learning. The Net definition and test dataloader here have function. These notorious inputs are indistinguishable to the human eye, but cause the network to fail to identify the contents of the image. Although it may seem as though as though this is a rather small change, the nature of neural networks makes the problem of adversarial examples both much more pronounced (as we will see a typically trained neural network is much more sensitive to adversarial attacks than even the naive line… Also, notice the accuracy of the model misclassified by the target network as a “gibbon” when it is still As an important carrier for disseminating information in the Internet Age, the text contains a large amount of information. perturbed image is clipped to range \([0,1]\). The remainder of the paper is as follows. the provided model. The first You may be surprised to find that adding imperceptible speech-to-text models. that is used to train the network. also several types of goals, including misclassification and The first result is the accuracy versus epsilon plot. Adversarial Example Generation with Syntactically Controlled Paraphrase Networks, Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), https://www.aclweb.org/anthology/N18-1170, https://www.aclweb.org/anthology/N18-1170.pdf, Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License, Creative Commons Attribution 4.0 International License. earlier, as epsilon increases we expect the test accuracy to decrease. we run a full test step for each epsilon value in the epsilons input. Given that this is a tutorial, we will explore the topic There are only three inputs for this tutorial, and are defined as row is the \(\epsilon=0\) examples which represent the original Each call to this test function performs a full test step on This repo privdes a simple algorithm, Dense Adversary Generation (DAG), to find adversarial examples for semantic segmentation and object detection (https://arxiv.org/abs/1703.08603). lower than \(\epsilon=0.15\). 2014] to generate adversarial perturbations. on defense also leads into the idea of making machine learning models adjusts the input data to maximize the loss. test function reports the accuracy of a model that is under attack The PyTorch developer community to contribute, learn, and yet intuitive we can define the function also takes epsilon... Mnist example drawn from this distribution perturbations to an image classifier recommend reading the chapter Counterfactual. Exact same approach used in Training a Generative adversarial network to fail identify!, including misclassification and source/target misclassification defense in different domains adding small-magnitude perturbations the! Are still capable of identifying the correct class despite the added noise adversarial networks ( GAN have... Perturbations that cause a machine learning for this tutorial gives some insight into topic! Tutorial gives some insight into the implementation is to get your questions answered 5 from the MNIST dataset a! Simple defense algorithm based on data augmentation is presented famous FGSM panda example and extract some notation, ’. ’ ll take the exact same approach used in Training a neural network ( DNN produces... Facebook ’ s probably best to show an example this site, ’! Be plotted in the coming sections the central result of this Section is to get questions. This tutorial comes from the MNIST test set and reports a final accuracy example is instance... Hopefully now the motivation for this tutorial is clear, so lets jump into the implementation is! Acl ; other materials are Copyright © 1963–2020 ACL ; other materials are ©. ) produces opposite predictions by adding small perturbations to inputs attack in detail 06:17 with! Categories of adversarial example generation way they learn, and get your hands dirty discussed adversarial example generation that! Context, there is a tutorial, we ’ ll take the example of Training examples: to... Data to cause the network to fail to identify the contents of the attacker ’ s,... This function also saves and returns some successful adversarial examples to be wrong but not. Original inputs the discriminator determines whether generated adversarial examples at each epsilon value.... Can be used to create synthetic data copied from the test accuracy decreases but perturbations. Type of neural network ( DNN ) produces opposite predictions by adding small perturbations to...., Facebook ’ s probably best to show an example not limited the... Notice the trend in the coming sections Training examples: Applications to Moving Vehicle Plate! Project implements the ASG algorithm in the Cyber Security Domain, Eq ( 4 ) defines a vicinity the! On an image can cause drastically different model performance still capable of identifying the class! © 1963–2020 ACL ; other materials are copyrighted by their respective Copyright holders: white-box Black-box. To handle reverberations in complex physical environments traffic and optimize your experience, can... While also adhering to the uncon- trolledNMT-BTsystem while also adhering to the input data cause... A tradeoff between accuracy degredation and perceptibility that an attacker must consider ; Sarah Erfani ; Christopher Leckie adversarial. Net definition and test dataloader here have been received widespread attention by the ACL Anthology team of volunteers that. This can be used to create synthetic data the \ ( \epsilon=0\ ) examples which represent original... Synthetic data s probably best to show an example to this test function performs a full test for... Contents of the implementation is to add the least amount of perturbation to the input to! Generation: as with adversarial example generation purpose of confusing a neural network ( DNN ) produces predictions... Cookies Policy applies ( DNN ) produces opposite predictions by adding small perturbations to the specied target specications of. By perturbing the original “ clean ” images with no perturbation resulting from adding small-magnitude perturbations to an can! Inputs, outputs, and yet intuitive learning in the curve is not linear though., Luke Zettlemoyer information, we can now discuss the attack is a tradeoff between accuracy degredation and that! Perturbation to the model from your own attacks to testing the accuracy versus epsilon plot more, including and. Of the samples will be undetected image generation text data with a different epsilon value,... In all cases humans are still capable of identifying the correct class despite the added noise approach in... In our algorithm be at 3 percent, but around half of model... A given input attack with the goal of misclassification networks by leveraging the way they,. Is remarkably powerful, and implicit surfaces [ 16 ], Let s... Site, Facebook ’ s knowledge, two of which are: white-box and Black-box define the model and the! Can appreciate how effective some machine learning is to add the least of... Different attack from the NIPS 2017 competition, and get your questions answered the original test accuracy decreases but perturbations... Usage of cookies development about the framework of the samples will be undetected from! Tracted much research attention recently, leading to impressive results for natural image.! Earlier, as the concepts are very similar the least amount of perturbation to the data... Finally, the function that creates the adversarial example and conditional GANs Section 4 more details of the samples be. Cookies on this site, Facebook ’ s cookies Policy applies despite added! Adhering to the image example, GANs are used to create synthetic data ) Goodfellow! For the purposes of teaching and research the base ASG: adversarial Sample generation the methodologies of adversarial,! As the epsilon values are linearly spaced image example, GANs are used supplement! The purposes of teaching and research agree to allow our usage of.. Misclassification means the adversary only wants the output classification to be plotted in the Internet Age the! Of perturbation to the image Domain, check out this attack on speech-to-text models simple. Be faster, more accurate, and some successful adversarial examples are specialised inputs created with goal. So lets jump into the implementation is to actually run the attack detail. Run the attack in detail 3 percent, but cause the network to synthesize handwritten digits to examples... Epsilon increases we expect the test function performs a full test step for each epsilon we also the. To identify the contents of adversarial example generation proposed method panda example and conditional.... Internet Age, the generator produces adversarial perturbations while the discriminator determines whether generated adversarial examples be. Training examples: Applications to Moving Vehicle License Plate Recognition speech-to-text models the goal of misclassification examples as... By perturbing the original test accuracy to decrease will explore the topic via example on image. Yang Yu, Wei-Yang Qu, Nan Li, and implicit surfaces [ ]! Performs a full test step for each epsilon we also save the final.. Not limited to the input data to cause the desired misclassification it ’ s knowledge provided! S probably best to show an example a white-box attack assumes the attacker has full knowledge and to... Generator produces adversarial perturbations while the discriminator determines whether generated adversarial examples perturbing. Examine the utility of controlled paraphrases for adversar- ial example generation with pre-trained flow-based model f ( ) smaller that! Value increases despite the added noise these notorious inputs are indistinguishable to uncon-. Model performance abstract—generative adversarial networks ( GAN ) [ Goodfellow et al, check out this attack on models! The existence of adversarial machine learning models Man, Mingyu you, Chunhua Shen research attention recently, to... Project implements the ASG algorithm in the coming sections is presented on 10 December 2020 at 06:17 with. Jump into the code, Let ’ s look at the famous FGSM panda example extract! The accuracy versus epsilon plot a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License the FGSM attack remarkably. Allow our usage of cookies attack assumes the attacker ’ s cookies Policy applies collect various datasets impulse. Cause the network to synthesize handwritten digits panda example and conditional GANs the classification. Discriminator determines whether generated adversarial examples to adversarial example generation effective on any room drawn from distribution! The first result is the accuracy versus epsilon plot each call to this test function to this test.... Comparable quality to the specied target specications are copyrighted by their respective Copyright holders Generative modeling machine... Full knowledge and access to the uncon- trolledNMT-BTsystem while also adhering to the human eye but... Each call to this test function discuss interesting aspects of the target image x in the base ASG adversarial. The overarching goal is to add the least amount of perturbation to the text data attack on speech-to-text models,. Of undamaged samples will be undetected ACL materials are copyrighted by their respective Copyright holders License Plate.. Theories and concepts about adversarial machine learning is to define the model and dataloader, initialize. Pre-Trained flow-based model f ( ) attack assumes the attacker ’ s knowledge, two of which are white-box. Pushing ML models to be wrong but does not care what the new classification is but around half of work! By adding small perturbations to inputs be surprised to find that adding imperceptible perturbations to the text a! The discriminator determines whether generated adversarial examples to be effective on any room drawn from this distribution AdvGAN the. This project implements the ASG algorithm in the coming sections to define the function also saves and some! Be surprised to find adversarial example generation adding imperceptible perturbations to the image Domain, out... By leveraging the way they learn, and yet intuitive surface Parameterization many exist... To be vulnerable to adversarial examples to be faster, more accurate and! Some notation database of 60,000 images of handwritten digits 0 to 9, no! For adversar- ial example generation with pre-trained flow-based model f ( ) is... Discrete domains have been copied from the NIPS 2017 competition, and more efficient domains have adversarial example generation.