Biblio

Deep learning technologies, which are the key components of state-of-the-art Artificial Intelligence (AI) services, have shown great success in providing human-level capabilities for a variety of tasks, such as visual analysis, speech recognition, and natural language processing and etc. Building a production-level deep learning model is a non-trivial task, which requires a large amount of training data, powerful computing resources, and human expertises. Therefore, illegitimate reproducing, distribution, and the derivation of proprietary deep learning models can lead to copyright infringement and economic harm to model creators. Therefore, it is essential to devise a technique to protect the intellectual property of deep learning models and enable external verification of the model ownership. In this paper, we generalize the "digital watermarking'' concept from multimedia ownership verification to deep neural network (DNNs) models. We investigate three DNN-applicable watermark generation algorithms, propose a watermark implanting approach to infuse watermark into deep learning models, and design a remote verification mechanism to determine the model ownership. By extending the intrinsic generalization and memorization capabilities of deep neural networks, we enable the models to learn specially crafted watermarks at training and activate with pre-specified predictions when observing the watermark patterns at inference. We evaluate our approach with two image recognition benchmark datasets. Our framework accurately (100$\backslash$%) and quickly verifies the ownership of all the remotely deployed deep learning models without affecting the model accuracy for normal input data. In addition, the embedded watermarks in DNN models are robust and resilient to different counter-watermark mechanisms, such as fine-tuning, parameter pruning, and model inversion attacks.

Using mobile sinks to collect sensed data in WSNs (Wireless Sensor Network) is an effective technique for significantly improving the network lifetime. We investigate the problem of collecting sensed data using a mobile sink in a WSN with unreachable regions such that the network lifetime is maximized and the total tour length is minimized, and propose a polynomial-time heuristic, an ILP-based (Integer Linear Programming) heuristic and an MINLP-based (Mixed-Integer Non-Linear Programming) algorithm for constructing a shortest path routing forest for the sensor nodes in unreachable regions, two energy-efficient heuristics for partitioning the sensor nodes in reachable regions into disjoint clusters, and an efficient approach to convert the tour construction problem into a TSP (Travelling Salesman Problem). We have performed extensive simulations on 100 instances with 100, 150, 200, 250 and 300 sensor nodes in an urban area and a forest area. The simulation results show that the average lifetime of all the network instances achieved by the polynomial-time heuristic is 74% of that achieved by the ILP-based heuristic and 65% of that obtained by the MINLP-based algorithm, and our tour construction heuristic significantly outperforms the state-of-the-art tour construction heuristic EMPS.