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With the increasing awareness of citizen copyright, more and more images containing watermarks are appearing in daily life. However, existing research shows that images with watermarks can cause neural network misclassification, posing a significant threat to the popularization and application of neural networks. Adversarial training is one of the defensive methods to solve this problem, but it requires a large number of watermark adversarial samples as training data. To address this issue, this paper proposes a visible watermark adversarial attack method based on intelligent evolutionary algorithm to generate high-intensity watermark adversarial samples. This method can not only quickly generate watermark adversarial samples, but also maximize the attack on the neural network. In addition, this method incorporates image quality evaluation metrics to constrain the visual loss of the image, making the watermark adversarial samples more visually appealing. The comprehensive experimental results show that the proposed method has lower time complexity than the benchmark watermark attack method, and has a higher attack rate on neural networks compared to the benchmark black box attack. [ABSTRACT FROM AUTHOR]

Two of the most important task metrics that measure data-mining performance specific to big data: one is real-time and the other is accuracy. The stream data flows from data generation to message queue and then into Flink through data source for calculation. In this process, due to different network transmission speed and different computing performance of different nodes, the sequence of stream data entering the computing framework and the time sequence of events generated by data will be partially out of order. The traditional watermark mechanism for window-facing operations cannot consider the real-time performance and accuracy of the operation results in the case of streaming data with uncertain out-of-order degree. To solve this problem, a stream data microcluster model is established. Based on the local out-of-order degree of stream data event time, the out-of-order degree of stream data representing the current moment is calculated by the local out-of-order degree algorithm. A dynamic watermark adjustment strategy is designed to adjust the watermark dynamically according to the degree of flow data disorder. Finally, the dynamic watermark adjustment strategy based on event time window is implemented in Apache Flink framework. Experimental results show that the dynamic watermark adjustment strategy based on event time window can effectively consider the accuracy and real-time performance of window operation under the condition of elastic or uncertain chaotic flow data. [ABSTRACT FROM AUTHOR]

A semi-fragile watermarking scheme based on toral automorphisms was proposed for image authentication and copyright protection. According to torus automorphism, the image's DCT coefficients and copyright information are used to generate the secure semi-fragile watermark, and the torus automorphism also decides which region of the image the watermark should be embedded into. The authenticator acquires the copyright information from the received watermarked image and indicates the specific locations that have been malicious manipulations. The proposed scheme can accept quantization based lossy compression to a determined degree. [ABSTRACT FROM AUTHOR]