Neural Network Watermarking: A Comprehensive Review

Deep learning has become a vital part of our daily lives, and its applications are increasing exponentially. However, this rapid growth has also raised concerns about the safety and security of these systems. One major threat to deep learning is the presence of imposter classifiers, which can compromise the accuracy and reliability of the system. To tackle this issue, researchers have proposed using watermarking techniques to detect these imposter classifiers.
Watermarking is like a digital fingerprint that can be added to the output of a deep learning model. This fingerprint can be used to identify any unauthorized changes or tampering in the system. The article discusses how watermarking can be used to detect imposter classifiers in deep learning systems and the different approaches that can be taken to implement this technique.
The article starts by explaining the importance of detecting imposter classifiers in deep learning systems. It highlights the risks associated with these imposters, such as data poisoning attacks, and how they can compromise the accuracy and reliability of the system. The article then provides a detailed overview of watermarking techniques and their applications in deep learning.
One of the key points discussed in the article is the difference between hardware-based and software-based watermarking. Hardware-based watermarking involves adding a watermark directly to the hardware components of the system, such as thechiplets used in modern computing systems. Software-based watermarking, on the other hand, involves adding a watermark to the software that runs on the system.
The article also discusses various approaches to implementing watermarking in deep learning systems, including the use of neural networks and statistical methods. These approaches are compared based on their advantages and disadvantages, such as computational complexity and robustness against attacks.
To illustrate the effectiveness of watermarking, the article provides several examples of its application in real-world scenarios. For instance, watermarking can be used to detect imposter classifiers in image classification tasks, where an attacker might try to replace a legitimate classifier with a malicious one that mislabels images.
The article concludes by emphasizing the importance of developing robust and secure deep learning systems that can protect against various types of attacks. It highlights the potential benefits of using watermarking techniques in this regard, such as improved system reliability and reduced risk of data poisoning attacks.
Overall, the article provides a comprehensive overview of the use of watermarking in deep learning systems to detect imposter classifiers. By using analogies and simple language, it demystifies complex concepts related to watermarking and threat models, making it accessible to readers without prior knowledge of these topics.