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How do you protect your edge AI models from attacks and noise?

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Edge AI models are designed to run on devices with limited resources, such as smartphones, cameras, or sensors. They can offer faster, cheaper, and more secure solutions for various applications, such as face recognition, object detection, or speech recognition. However, edge AI models also face challenges from attacks and noise that can compromise their performance and reliability. How do you protect your edge AI models from these threats? Here are some tips and best practices to consider.

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Haris K.

Helping businesses grow with the power of Agile.
Riccardo Zanardelli

Head of Data Science @Beretta | PhD Candidate, Statistics and Data Science | Engineer | Only personal views here
Mert Tugay KAYMAZ

???? Full Stack Developer - Python | PHP | .Net | Java | React ???? Data Scientist & Analyst ?? Researcher

1 Define your threat model

The first step to protect your edge AI models is to define your threat model, which specifies the assumptions, goals, and capabilities of the potential attackers. For example, you can ask yourself: Who are the attackers? What are their motivations? How can they access your model or device? What type of attacks can they launch? How can you detect and respond to them? By defining your threat model, you can identify the most relevant and realistic risks and prioritize your defense strategies accordingly.

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Haris K.

Helping businesses grow with the power of Agile.
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Attacks can be detected by monitoring the model's performance and by using security tools such as intrusion detection systems. Respond to attacks by isolating the affected device, removing the malicious data, and updating the model's parameters.

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Riccardo Zanardelli

Head of Data Science @Beretta | PhD Candidate, Statistics and Data Science | Engineer | Only personal views here
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Of course, sketching a threat model is the most important step to secure your model on the edge. Nonetheless, adversarial attacks can be hard to detect, as when they are sophisticated enough it might be hard to distinguish them from regular interactions. If you work with federated learning, make sure that you implement proper defence against model update with local data that might come from malicious activity.

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2 Train your model robustly

The second step to protect your edge AI models is to train your model robustly, which means to make it resilient to various types of attacks and noise. For example, you can use techniques such as adversarial training, data augmentation, regularization, or pruning to improve your model's generalization and robustness. You can also use tools such as Robust Vision Benchmark or IBM Adversarial Robustness Toolbox to evaluate and enhance your model's robustness against different attack scenarios.

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Supawit Kansompoj
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One thing I've found helpful is to apply the DevSecOp process into the model training pipeline to ensure that there no either malicious codes or vulnerabilities in your model's code.

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3 Encrypt your model and data

The third step to protect your edge AI models is to encrypt your model and data, which means to prevent unauthorized access or modification of your model or data. For example, you can use techniques such as homomorphic encryption, secure multiparty computation, or federated learning to encrypt your model or data during training, inference, or communication. You can also use tools such as TensorFlow Privacy or PySyft to implement these techniques in your edge AI projects.

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Mert Tugay KAYMAZ

???? Full Stack Developer - Python | PHP | .Net | Java | React ???? Data Scientist & Analyst ?? Researcher
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Exactly, encrypting edge AI models and data is vital for security. Techniques like homomorphic encryption, secure multiparty computation, and federated learning enhance protection during training, inference, and communication. Tools like TensorFlow Privacy and PySyft assist in implementing these techniques effectively, safeguarding your edge AI projects from unauthorized access and modification, and ensuring the confidentiality and integrity of your model and data.

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4 Update your model regularly

The fourth step to protect your edge AI models is to update your model regularly, which means to keep your model up to date with the latest data, features, or security patches. For example, you can use techniques such as online learning, incremental learning, or transfer learning to update your model dynamically and adaptively. You can also use tools such as TensorFlow Lite or PyTorch Mobile to deploy and update your model on edge devices easily and efficiently.

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5 Monitor your model performance

The fifth step to protect your edge AI models is to monitor your model performance, which means to track and analyze your model's behavior and results. For example, you can use techniques such as anomaly detection, confidence estimation, or explainability to monitor your model's performance and detect any anomalies, errors, or biases. You can also use tools such as TensorFlow Model Analysis or Captum to visualize and understand your model's performance on edge devices.

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Farhad Dastmalchi

Principal Scientist, Immunology, Oncology , Clinical Biomarker and Clinical Informatics Expert
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Key points to consider: Continuous Surveillance: Regularly track your model's behavior and results to identify potential vulnerabilities and deviations. Anomaly Detection: Employ techniques to flag unusual behavior or results that might indicate attacks or noise affecting your model. Bias and Fairness Assessment: Use explainability methods to ensure your model's decisions remain unbiased and fair across different groups. Visual Tools: Tools like TensorFlow Model Analysis and Captum provide visual insights into your model's performance, aiding in effective analysis and decision-making.

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6 Harden your device security

The sixth step to protect your edge AI models is to harden your device security, which means to strengthen the physical and software protection of your edge devices. For example, you can use techniques such as tamper detection, secure boot, or encryption to harden your device security and prevent unauthorized access or modification of your device or model. You can also use tools such as Arm TrustZone or Intel SGX to enable hardware-based security features on your edge devices.

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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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How do you protect your edge AI models from attacks and noise?

1

2

3

4

5

6

7

1 Define your threat model

2 Train your model robustly

3 Encrypt your model and data

4 Update your model regularly

5 Monitor your model performance

6 Harden your device security

7 Here’s what else to consider

Artificial Intelligence

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How do you protect your edge AI models from attacks and noise?

1

2

3

4

5

6

7

1 Define your threat model

2 Train your model robustly

3 Encrypt your model and data

4 Update your model regularly

5 Monitor your model performance

6 Harden your device security

7 Here’s what else to consider

Artificial Intelligence

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