Search

Copy-move is a common image forgery operation, which copies and moves a block of an image from one position to another place. Image hashing refers to extracting a unique number sequence from the image by using various image features. In practical application, image hashing is used to replace the image itself, which effectively reduces the cost of image storage and computational complexity. In this paper, we propose a novel image hash extraction scheme: constructing image hashing by combining local feature based on Canny operator and global feature based on tensor. In addition, instead of using the traditional correlation coefficient or Hamming distance, a novel method is proposed to calculate the hash distances. A large number of experiments have proved that our image hashing can achieve a better balance between robustness and discrimination with a shorter hash length. What's more, we can directly locate the forgery areas from the hashing for copy-move forged images. [ABSTRACT FROM AUTHOR]

Intrusion detection systems play a very important role in industrial Internet network security. However, in the large-scale, complex, and heterogeneous industrial Internet of Things (IoT), it is becoming more and more difficult to defend network intrusion threats due to the insufficiency of high-quality attack samples. To solve the problem, an efficient federated network intrusion method called EFedID is proposed for industrial IoT, which can allow different industrial agents to collaboratively train a comprehensive detection model. Specifically, the adaptive gradient sparsification method is introduced to alleviate the communication and computation overheads. To protect the data privacy of the agents, a CKKS cryptosystem-based secure communication protocol is designed to encrypt the model parameters through the federated training process. Our proposed system demonstrates exceptional detection performance on the NSL-KDD, KDD CUP 99, and CICIDS 2017 datasets. Notably, on the NSL-KDD dataset, the model compression rate reaches 9 times while the model accuracy reaches 84.31%. On the KDD CUP 99 dataset, the model compression rate reaches 8.9 times while the model accuracy reaches 97.3%. Lastly, on the CICIDS 2017 dataset, the model compression rate reached 6.173 times while the model accuracy reached 95.51%. The experimental results demonstrate that the proposed method is very suitable for effectively developing a high-accuracy detection model while protecting the data information of industrial agents. Furthermore, the method can be extended to other recent deep learning networks for intrusion detection. [ABSTRACT FROM AUTHOR]