You are currently viewing The MPAI 2022 Calls for Technologies – Part 3 (Neural Network Watermarking)

The MPAI 2022 Calls for Technologies – Part 3 (Neural Network Watermarking)

Research, personnel, training and processing can bring the development costs of a neural network anywhere from a few thousand to a few hundreds of thousand dollars. Therefore, the AI industry needs a technology to ensure traceability and integrity not only of a neural network, but also of the content generated by it (so-called inference). The content industry facing a  similar problem, has used watermarking to imperceptibly and persistently insert a payload carrying, e.g., owner ID, timestamp, etc. to signal the ownership of a content item. Watermarking can also be used by the AI industry.

The general requirements for using watermarking in neural networks are:

  • The techniques shall not affect the performance of the neural network.
  • The payload shall be recoverable even if the content was modified.

MPAI has classified the cases of watermarking use as follows:

  • Identification of actors (i.e., neural network owner, customer, and end-user).
  • Identification of the neural network model.
  • Detecting the modification of a neural network.

This classification is depicted in Figure 1 and concerns the use of watermarking technologies in neural networks and is independent of the intended use.

Figure 1 – Classification of neural network watermarking uses

MPAI has identified the need for a standard – code name MPAI-NNW – enabling users to measure the performance of the following component of a watermarking technology:

  • The ability of a watermark inserter to inject a payload without deteriorating the performance of the Neural Network.
  • The ability of a watermark detector to ascertain the presence and of a watermark decoder to retrieve the payload of the inserted watermark when applied to:
    • A modified watermarked network (e.g., by transfer learning or pruning).
    • An inference of the modified model.
  • The computational cost (e.g., execution time) of a watermark inserter to inject a payload, a watermark detector/decoder to detect/decode a payload from a watermarked model or from any of its inferences.

Figure 2 depicts the three watermarking components covered by MPAI-NNW.

Figure 2 – The three areas to be covered by MPAI-NNW

MPAI has issued a Call to acquire the technologies for use in the standard. The list below is a subset of the requests contained in the call:

  • Use cases
    • Comments on use cases.
  • Impact of the watermark on the performance
    • List of Tasks to be performed by the Neural Network (g. classification task, speech generation, video encoding, …).
    • Methods to measure the quality of the inference produced (g. precision, recall, subjective quality evaluation, PSNR, …).
  • Detection/Decoding capability
    • List of potential modifications that a watermark shall be robust against (g. pruning, fine-tuning, …).
    • Parameters and ranges of proposed modifications.
    • Methods to evaluate the differences between the original and retrieved watermarks (g., Symbol Error Rate).
  • Processing cost
    • Specification of the testing environments.
    • Specification of the values characterizing the processing of Neural Networks.

Below are a few useful links for those wishing to know more about the MPAI-NNW Call for Technologies and how to respond to it:

The MPAI secretariat shall receive the responses to the MPAI-NNW Call for Technologies by 2022 October