A Survey on Model Watermarking Neural Networks

Machine learning (ML) models are applied in an increasing variety of domains. The availability of large amounts of data and computational resources encourages the development of ever more complex and valuable models. These models are considered intellectual property of the legitimate parties who have trained them, which makes their protection against stealing, illegitimate redistribution, and unauthorized application an urgent need. Digital watermarking presents a strong mechanism for marking model ownership and, thereby, offers protection against those threats. The emergence of numerous watermarking schemes and attacks against them is pushed forward by both academia and industry, which motivates a comprehensive survey on this field. This document at hand provides the first extensive literature review on ML model watermarking schemes and attacks against them. It offers a taxonomy of existing approaches and systemizes general knowledge around them. Furthermore, it assembles the security requirements to watermarking approaches and evaluates schemes published by the scientific community according to them in order to present systematic shortcomings and vulnerabilities. Thus, it can not only serve as valuable guidance in choosing the appropriate scheme for specific scenarios, but also act as an entry point into developing new mechanisms that overcome presented shortcomings, and thereby contribute in advancing the field.

Tracking all members of a honey bee colony over their lifetime

Computational approaches to the analysis of collective behavior in social insects increasingly rely on motion paths as an intermediate data layer from which one can infer individual behaviors or social interactions. Honey bees are a popular model for learning and memory. Previous experience has been shown to affect and modulate future social interactions. So far, no lifetime history observations have been reported for all bees of a colony. In a previous work we introduced a tracking system customized to track up to $4000$ bees over several weeks. In this contribution we present an in-depth description of the underlying multi-step algorithm which both produces the motion paths, and also improves the marker decoding accuracy significantly. We automatically tracked ${sim}2000$ marked honey bees over 10 weeks with inexpensive recording hardware using markers without any error correction bits. We found that the proposed two-step tracking reduced incorrect ID decodings from initially ${sim}13%$ to around $2%$ post-tracking. Alongside this paper, we publish the first trajectory dataset for all bees in a colony, extracted from ${sim} 4$ million images. We invite researchers to join the collective scientific effort to investigate this intriguing animal system. All components of our system are open-source.