KEYWORDS: Digital watermarking, Data modeling, RGB color model, Copyright, Education and training, Binary data, Neural networks, Image classification, Process modeling, Visibility
With the growing number of solutions based on deep learning methods, there is a need to protect pretrained models against unauthorized distribution. For deep model watermarking, one of the most important criteria is to maintain the accuracy of predictions after embedding the protective information. In this paper, we propose a black-box watermarking method based on fine-tuning image classification models on a watermarking dataset, which is synthesized by superimposing pseudo-holograms on images of the original dataset. The proposed method allows to preserve the initial quality of classification, in addition, a series of experiments for five different models showed the invariance of the method to the architecture of a deep neural network. The conducted simulation of the most common attacks on watermarked models shows that adversarial attempts to completely remove the watermark are improbable without significant loss of model accuracy. Additionally, experimental results contain the selection of parameters, such as the number of triggers and original images in watermarking dataset, allowing to increase method efficiency.
KEYWORDS: Digital watermarking, Neural networks, Data modeling, Process modeling, Legal, RGB color model, Binary data, Visibility, Information security
In this paper, a new method for protection of copyright on pretrained deep neural networks is proposed. The main idea is to embed a digital watermark into a pretrained model by finetuning the final layer weights. A deep neural network is retrained on a unique trigger set formed by synthesizing pseudo-holographic images and embedding them into raster images of the original dataset. In order to provide the accuracy of the original model, the deep model watermarking process is implemented with addition of a new class intended for the elements of the trigger sample. Experimental results show that the quality of the original model is not affected by watermarking process. Furthermore, the model can be retrained to distinguish the watermark of a legal owner from unauthorized one.
In this paper, we propose a new digital watermarking method for protection of vector maps in geographic information systems. The method is based on a combination of two novel approaches. Firstly, the watermark is embedded into polygon objects of the map by cyclically shifting the vertex list of each polygon. Secondly, a raster image, superimposed on the map, is considered as a watermark. The major advantage of this method is that, unlike most existing watermarking techniques, it does not distort the map by altering the coordinate values. Experimental results demonstrate the efficiency of the proposed method, as well as the robustness of the embedded watermark against common geometric transformations: translation, scaling, rotation and cropping.
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