Robust Vehicle Tracking Using Perceptual Hashing Algorithm

2015 ◽  
Author(s):  
Zheng Li ◽  
Jian-Fei Yang ◽  
Long Chen ◽  
Juan Zha
Keyword(s):  
Vehicle Tracking ◽  
Perceptual Hashing ◽  
Hashing Algorithm

scholarly journals AAU-Net: Attention-Based Asymmetric U-Net for Subject-Sensitive Hashing of Remote Sensing Images

2021 ◽  
Vol 13 (24) ◽  
pp. 5109
Author(s):  
Kaimeng Ding ◽  
Shiping Chen ◽  
Yu Wang ◽  
Yueming Liu ◽  
Yue Zeng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Asymmetric Structure ◽  
Learning Models ◽  
Remote Sensing Images ◽  
Structure Characteristics ◽  
Special Subset ◽  
The Subject ◽  
Perceptual Hashing ◽  
Hashing Algorithm

The prerequisite for the use of remote sensing images is that their security must be guaranteed. As a special subset of perceptual hashing, subject-sensitive hashing overcomes the shortcomings of the existing perceptual hashing that cannot distinguish between “subject-related tampering” and “subject-unrelated tampering” of remote sensing images. However, the existing subject-sensitive hashing still has a large deficiency in robustness. In this paper, we propose a novel attention-based asymmetric U-Net (AAU-Net) for the subject-sensitive hashing of remote sensing (RS) images. Our AAU-Net demonstrates obvious asymmetric structure characteristics, which is important to improve the robustness of features by combining the attention mechanism and the characteristics of subject-sensitive hashing. On the basis of AAU-Net, a subject-sensitive hashing algorithm is developed to integrate the features of various bands of RS images. Our experimental results show that our AAU-Net-based subject-sensitive hashing algorithm is more robust than the existing deep learning models such as Attention U-Net and MUM-Net, and its tampering sensitivity remains at the same level as that of Attention U-Net and MUM-Net.

scholarly journals Image Hashing for Tamper Detection with Multiview Embedding and Perceptual Saliency

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Ling Du ◽  
Zhen Chen ◽  
Yongzhen Ke
Keyword(s):  
Semantic Information ◽  
Semantic Content ◽  
Tamper Detection ◽  
Image Hashing ◽  
High Quality ◽  
Multiple Features ◽  
Memory Efficiency ◽  
Benchmark Datasets ◽  
Perceptual Hashing ◽  
Hashing Algorithm

Perceptual hashing technique for tamper detection has been intensively investigated owing to the speed and memory efficiency. Recent researches have shown that leveraging supervised information could lead to learn a high-quality hashing code. However, most existing methods generate hashing code by treating each region equally while ignoring the different perceptual saliency relating to the semantic information. We argue that the integrity for salient objects is more critical and important to be verified, since the semantic content is highly connected to them. In this paper, we propose a Multi-View Semi-supervised Hashing algorithm with Perceptual Saliency (MV-SHPS), which explores supervised information and multiple features into hashing learning simultaneously. Our method calculates the image hashing distance by taking into account the perceptual saliency rather than directly considering the distance value between total images. Extensive experiments on benchmark datasets have validated the effectiveness of our proposed method.

scholarly journals Convolutional Neural Network Copy Detection with Neural Network Perceptual Hashing

2021 ◽  
Author(s):  
Chen Haozhe
Keyword(s):  
Neural Network ◽  
Test Model ◽  
Copy Detection ◽  
Neural Network Models ◽  
Ip Protection ◽  
Model Library ◽  
Perceptual Hashing ◽  
Hashing Algorithm ◽  
Hash Codes ◽  
Query Model

In recent years, many model intellectual property (IP) proof methods for IP protection have been proposed, such as model watermarking and model fingerprinting. However, as an important part of the model IP protection system, the model copy detection task has not received enough attention. With the increasing number of neural network models transmitted and deployed on the Internet, the search for similar models is in great demand, which concurrently triggers the security problem of copy detection of models for IP protection. Due to the high computational complexity, both model watermarking and model fingerprinting lack the capability to efficiently find suspected infringing models among tens of millions of models. In this paper, inspired by the hash-based image retrieval methods, we propose a perceptual hashing algorithm for convolutional neural networks (CNNs). The proposed perceptual hashing algorithm can convert the weights of CNNs to fixed-length binary hash codes so that the lightly modified version has the similar hash code as the original model. By comparing the similarity of a pair of hash codes between a query model and a test model in the model library, the similar versions of a query model can be retrieved efficiently. To the best of our knowledge, this is the first perceptual hashing algorithm for CNNs. The experiment performed on a model library containing 3,565 models indicates that our proposed perceptual hashing scheme has a superior copy detection performance.

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