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How can you improve the robustness of neural networks to adversarial attacks?

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Neural networks are powerful machine learning models that can learn complex patterns from data and perform various tasks, such as image recognition, natural language processing, and speech synthesis. However, they are also vulnerable to adversarial attacks, which are malicious inputs that are designed to fool or degrade the performance of the network. For example, a slight perturbation of an image can cause a neural network to misclassify it as a different object. This can have serious implications for applications that rely on the accuracy and reliability of neural networks, such as self-driving cars, medical diagnosis, and cybersecurity. Therefore, it is important to improve the robustness of neural networks to adversarial attacks and prevent potential harm or exploitation. In this article, you will learn some of the techniques and strategies that can help you achieve this goal.

本文章的要点总结

Adversarial training:

Incorporate both normal and purposely manipulated inputs during the training of your neural network. This teaches it to recognize and resist sneaky, harmful data alterations, much like a flu shot preps your immune system for the real deal.
Feature squeezing:

Implement feature squeezing by simplifying input features – like reducing image color complexity. It's like closing loopholes that adversaries exploit, making it tougher for them to sneak in harmful data undetected.

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Praful Zaru

CEO & Fullstack Developer | Helping…
Amit Pandey

Co-Founder, CTO @ Chayan.AI | Data…

1 Adversarial examples

An adversarial example is an input that is slightly modified from a normal input, such as adding some noise or distortion, but causes a significant change in the output of the network. For instance, an adversarial example of a panda image can look almost identical to a human eye, but make the network classify it as a gibbon. Adversarial examples can be generated by various methods, such as gradient-based optimization, genetic algorithms, or adversarial networks. The main challenge is to find the minimal perturbation that can fool the network, while keeping the input realistic and imperceptible.

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Praful Zaru

CEO & Fullstack Developer | Helping Businesses with Web and SaaS Solutions | Leading a Team of Experts in React.js, Vue.js, PHP, Tailwind.css, Next.js, TypeScript, Node.js, Laravel, and Python
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To bolster neural networks against adversarial attacks, employ adversarial training, use regularization to avoid overfitting, apply gradient masking to hinder attack calculations, and utilize ensemble models for consensus defense. These combined tactics enhance network resilience.

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Benjamin Crisp

Technology Innovator, full stack engineer & architect, Pega Design leader - CLSA. I have a passion for technology and raising up the best talent.
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lets all look back 20 years and learn from how people poisoned email spam filters in the exact ways.. With the power of these things, it is sad that we have think so defensively, but here we are.

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2 Adversarial training

One of the most common and effective ways to improve the robustness of neural networks to adversarial attacks is adversarial training, which is a form of regularisation that incorporates adversarial examples into the training process. The idea is to expose the network to both normal and adversarial inputs during training, and make it learn to correctly classify both types of inputs. This way, the network can become more resilient to adversarial perturbations and generalise better to unseen inputs. Adversarial training can be applied to different types of neural networks, such as convolutional neural networks, recurrent neural networks, and generative adversarial networks.

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Amit Pandey

Co-Founder, CTO @ Chayan.AI | Data Scientist, NLP, Computer Vision, Generative AI, DevOps, Data Engineer, Blockchain, Product Management
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One technique I would recommend within adversarial training is ensemble adversarial training. This approach involves training the neural network on adversarial examples generated not just from the model itself but also from a diverse set of pre-trained models or an ensemble of models. The idea is to expose the network to a broader range of adversarial strategies during training, which can lead to improved robustness. While adversarial training is a well-known method for improving model robustness, the use of ensemble techniques within this framework is a more nuanced approach that can offer additional benefits in terms of robustness and generalization

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3 Defensive distillation

Another technique that can enhance the robustness of neural networks to adversarial attacks is defensive distillation, which is a form of knowledge distillation that transfers the knowledge from a larger and more complex network to a smaller and simpler network. The larger network is trained with a high temperature softmax function, which produces softer and smoother output probabilities, while the smaller network is trained with a lower temperature softmax function, which produces sharper and more confident output probabilities. The smaller network is then trained to mimic the larger network's output probabilities, rather than the true labels. This way, the smaller network can learn to be more robust to adversarial perturbations, as they have less impact on the softer probabilities.

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Amit Pandey

Co-Founder, CTO @ Chayan.AI | Data Scientist, NLP, Computer Vision, Generative AI, DevOps, Data Engineer, Blockchain, Product Management
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One technique that I would recommend is temperature scaling in the softmax layer. In defensive distillation, the softmax layer of the neural network is modified to include a temperature parameter. A higher temperature is used to produce soft probabilities from the original model, which are then used to train the distilled model. The temperature is reduced (often set to 1) during the inference phase. The choice of temperature during the training phase can affect the robustness of the distilled model. A higher temperature leads to softer probabilities, which can provide information about the model's confidence in its predictions. This information can help the distilled model learn a smoother, more generalizable decision boundary.

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4 Randomisation and transformation

A third technique that can improve the robustness of neural networks to adversarial attacks is randomisation and transformation, which is a form of input preprocessing that adds some randomness or variation to the input before feeding it to the network. For example, you can randomly resize, crop, rotate, or flip the input image, or apply some filters, such as Gaussian blur, noise, or contrast. This can make the input less susceptible to adversarial perturbations, as they can be diluted or distorted by the randomisation and transformation. Moreover, this can also increase the diversity and complexity of the input data, which can help the network learn more features and improve its generalisation ability.

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Amit Pandey

Co-Founder, CTO @ Chayan.AI | Data Scientist, NLP, Computer Vision, Generative AI, DevOps, Data Engineer, Blockchain, Product Management
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I would suggest "stochastic activation pruning" technique. It introduces randomness by randomly deactivating a subset of neuron activations during inference. This randomness makes the network's behavior less predictable, hindering an attacker's ability to craft effective perturbations. The level of randomness can be adjusted by controlling the probability distribution used for pruning. This technique, when combined with other defenses, can significantly improve the network's resilience to adversarial attacks.

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5 Detection and rejection

A fourth technique that can improve the robustness of neural networks to adversarial attacks is detection and rejection, which is a form of output postprocessing that identifies and discards the inputs that are likely to be adversarial. For example, you can use some metrics, such as confidence score, entropy, or distance, to measure the uncertainty or anomaly of the network's output, and compare it with a predefined threshold. If the metric exceeds the threshold, you can flag the input as adversarial and reject it, or request a human verification. This can prevent the network from making false or misleading predictions based on adversarial inputs, and reduce the risk of harm or exploitation.

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Amit Pandey

Co-Founder, CTO @ Chayan.AI | Data Scientist, NLP, Computer Vision, Generative AI, DevOps, Data Engineer, Blockchain, Product Management
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Try feature squeezing. The basic idea is to limit the degrees of freedom available to an adversary by squeezing out unnecessary input features. For example, in the context of image inputs, feature squeezing can involve reducing the color bit depth of each pixel or applying spatial smoothing filters. By doing this, the subtle perturbations introduced by an adversary become more detectable, as they are likely to be squeezed out or altered. As an input is passed through a feature squeezing module before being fed in the network, the output can be compared with the original input's output. If discrepancy between the two exceeds a threshold, the input can be flagged as potentially adversarial and subjected to further scrutiny or rejected.

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6 Diversity and redundancy

A fifth technique that can improve the robustness of neural networks to adversarial attacks is diversity and redundancy, which is a form of ensemble learning that combines multiple networks or models to make a final decision. For example, you can use different architectures, parameters, or training methods to create diverse networks, or use different inputs, features, or domains to create redundant networks. Then, you can aggregate the outputs of the networks using some methods, such as voting, averaging, or stacking. This can increase the robustness of the final output, as it is less likely that all the networks will be fooled by the same adversarial input, and more likely that the majority or the best network will make the correct prediction.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Ragnar Edholm

Product Development Leader | Software Development | Product Design | Team Development | Infrastructure Design
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Try to identify the adversarial clients/users. They are often done by bots, slow them down with rate limiting or block them. Let them move to an easier target.

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How can you improve the robustness of neural networks to adversarial attacks?

1

2

3

4

5

6

7

1 Adversarial examples

2 Adversarial training

3 Defensive distillation

4 Randomisation and transformation

5 Detection and rejection

6 Diversity and redundancy

7 Here’s what else to consider

Software Development

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How can you improve the robustness of neural networks to adversarial attacks?

1

2

3

4

5

6

7

1 Adversarial examples

2 Adversarial training

3 Defensive distillation

4 Randomisation and transformation

5 Detection and rejection

6 Diversity and redundancy

7 Here’s what else to consider

Software Development

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