DeepHider: A Multi-module and Invisibility Watermarking Scheme for Language Model

Natural language processing (NLP) technology has shown great economic value in business. However, a natural language processing model faces two problems: (1) the owner's models of NLP are vulnerable to the threat of pirated redistribution, which breaks the symmetry relation between model owners and consumers; (2) a stealer may replace the classification module for a watermarked model to satisfy his specific classification task, and remove the watermark existing in the model. For the first problem, a model-protection mechanism is needed to keep the symmetry from being broken. Currently, language model protection schemes based on black-box verification are easily detected by humans or anomaly detectors, thus preventing verification. To address this issue, the paper proposes a trigger sample set with triggerless mode. For the second problem, this paper proposes a new threat, which is to replace the model classification module and perform global fine-tuning on the model, and verifies the model ownership through a white-box approach. Meanwhile, we use the features of blockchain such as tamper-proof and traceability to prevent the ownership statement of stealers. Experiments show that the proposed scheme successfully verifies ownership with 100% watermark verification accuracy without affecting the original performance of the model, and has strong robustness and low False trigger rate.

PCPT and ACPT: Copyright Protection and Traceability Scheme for DNN Model

Deep neural networks (DNNs) have achieved tremendous success in artificial intelligence (AI) fields. However, DNN models can be easily illegally copied, redistributed, or abused by criminals, seriously damaging the interests of model inventers. Currently, the copyright protection of DNN models by neural network watermarking has been studied, but the establishment of a traceability mechanism for determining the authorized users of a leaked model is a new problem driven by the demand for AI services. Because the existing traceability mechanisms are used for models without watermarks, a small number of false positives is generated. Existing black-box active protection schemes have loose authorization control and are vulnerable to forgery attacks. Therefore, based on the idea of black-box neural network watermarking with the video framing and image perceptual hash algorithm, this study proposes a passive copyright protection and traceability framework PCPT using an additional class of DNN models, improving the existing traceability mechanism that yields a small number of false positives. Based on the authorization control strategy and image perceptual hash algorithm, using the authorization control center constructed using the detector and verifier, a DNN model active copyright protection and traceability framework ACPT is proposed. It realizes stricter authorization control, which establishes a strong connection between users and model owners, and improves the framework security. The key sample that is simultaneously generated does not affect the quality of the original image and supports traceability verification.

Computation Rate Maximum for Mobile Terminals in UAV-assisted Wireless Powered MEC Networks with Fairness Constraint

This paper investigates an unmanned aerial vehicle (UAV)-assisted wireless powered mobile-edge computing (MEC) system, where the UAV powers the mobile terminals by wireless power transfer (WPT) and provides computation service for them. We aim to maximize the computation rate of terminals while ensuring fairness among them. Considering the random trajectories of mobile terminals, we propose a soft actor-critic (SAC)-based UAV trajectory planning and resource allocation (SAC-TR) algorithm, which combines off-policy and maximum entropy reinforcement learning to promote the convergence of the algorithm. We design the reward as a heterogeneous function of computation rate, fairness, and reaching of destination. Simulation results show that SAC-TR can quickly adapt to varying network environments and outperform representative benchmarks in a variety of situations.

A deep Convolutional Neural Network for topology optimization with strong generalization ability

This paper proposes a deep Convolutional Neural Network(CNN) with strong generalization ability for structural topology optimization. The architecture of the neural network is made up of encoding and decoding parts, which provide down- and up-sampling operations. In addition, a popular technique, namely U-Net, was adopted to improve the performance of the proposed neural network. The input of the neural network is a well-designed tensor with each channel includes different information for the problem, and the output is the layout of the optimal structure. To train the neural network, a large dataset is generated by a conventional topology optimization approach, i.e. SIMP. The performance of the proposed method was evaluated by comparing its efficiency and accuracy with SIMP on a series of typical optimization problems. Results show that a significant reduction in computation cost was achieved with little sacrifice on the optimality of design solutions. Furthermore, the proposed method can intelligently solve problems under boundary conditions not being included in the training dataset.