Spot evasion attacks: Adversarial examples for license plate recognition systems with convolutional neural networks

The evolve of neural networks algorithm into deep learning convolutional neural networks seems like the next generation for object detection. This algorithm works has a significantly better accuracy and did not tied to any particular aspect ratio. License plate and traffic signs detection and recognition have a number of different applications relevant for transportation systems, such as traffic monitoring, detection of stolen vehicles, driver navigation support or any statistical research. An exponential increase in number of vehicles necessitates the use of automated systems to maintain vehicle information. The information is highly required for both management of traffic as well as reduction of crime. Number plate recognition is an effective way for automatic vehicle identification. A number of methods have been proposed, but only for particular cases and working under constraints (e.g. known text direction or high resolution). Deep learning convolutional neural networks work well especially in handles occlusion/rotation better, therefore we believe this approach is able to provide a better solution to the unconstrained license plate recognition problem.

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Automatic license plate recognition with convolutional neural networks trained on synthetic data

2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP) ◽

10.1109/mmsp.2017.8122260 ◽

2017 ◽

Cited By ~ 4

Author(s):

Tomas Bjorklund ◽

Attilio Fiandrotti ◽

Mauro Annarumma ◽

Gianluca Francini ◽

Enrico Magli

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Synthetic Data ◽

License Plate ◽

License Plate Recognition ◽

Automatic License Plate Recognition

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Bangla License Plate Recognition Using Convolutional Neural Networks (CNN)

2019 22nd International Conference on Computer and Information Technology (ICCIT) ◽

10.1109/iccit48885.2019.9038597 ◽

2019 ◽

Cited By ~ 1

Author(s):

M M Shaifur Rahman ◽

Moin Mostakim ◽

Mst Shamima Nasrin ◽

Md Zahangir Alom

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

License Plate ◽

License Plate Recognition

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APPLICATION OF THE CONVOLUTIONAL NEURAL NETWORKS FOR THE SECURITY OF THE OBJECT RECOGNITION IN A VIDEO STREAM

Cybersecurity Education Science Technique ◽

10.28925/2663-4023.2020.8.97112 ◽

2020 ◽

Vol 4 (8) ◽

pp. 97-112

Author(s):

Danylo Svatiuk ◽

Oksana Svatiuk ◽

Oleksandr Belei

Keyword(s):

Operating System ◽

Neural Networks ◽

Real Time ◽

Mobile Devices ◽

Convolutional Neural Networks ◽

Image Recognition ◽

Video Stream ◽

License Plate ◽

License Plate Recognition ◽

Android Operating System

The article is devoted to analyzing methods for recognizing images and finding them in the video stream. The evolution of the structure of convolutional neural networks used in the field of computer video flow diagnostics is analyzed. The performance of video flow diagnostics algorithms and car license plate recognition has been evaluated. The technique of recognizing the license plates of cars in the video stream of transport neural networks is described. The study focuses on the creation of a combined system that combines artificial intelligence and computer vision based on fuzzy logic. To solve the problem of license plate image recognition in the video stream of the transport system, a method of image recognition in a continuous video stream with its implementation based on the composition of traditional image processing methods and neural networks with convolutional and periodic layers is proposed. The structure and peculiarities of functioning of the intelligent distributed system of urban transport safety, which feature is the use of mobile devices connected to a single network, are described. A practical implementation of a software application for recognizing car license plates by mobile devices on the Android operating system platform has been proposed and implemented. Various real-time vehicle license plate recognition scenarios have been developed and stored in a database for further analysis and use. The proposed application uses two different specialized neural networks: one for detecting objects in the video stream, the other for recognizing text from the selected image. Testing and analysis of software applications on the Android operating system platform for license plate recognition in real time confirmed the functionality of the proposed mathematical software and can be used to securely analyze the license plates of cars in the scanned video stream by comparing with license plates in the existing database. The authors have implemented the operation of the method of convolutional neural networks detection and recognition of license plates, personnel and critical situations in the video stream from cameras of mobile devices in real time. The possibility of its application in the field of safe identification of car license plates has been demonstrated.

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Automatic Modular License Plate Recognition System Using Fast Convolutional Neural Networks

2020 13th International Conference "Management of large-scale system development" (MLSD) ◽

10.1109/mlsd49919.2020.9247817 ◽

2020 ◽

Author(s):

Konstantin D. Rusakov

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Recognition System ◽

License Plate ◽

License Plate Recognition

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Adv-Plate Attack: Adversarially Perturbed Plate for License Plate Recognition System

Journal of Sensors ◽

10.1155/2021/6473833 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Hyun Kwon ◽

Jang-Woon Baek

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Deep Neural Networks ◽

Recognition System ◽

License Plate ◽

Learning Technology ◽

License Plate Recognition ◽

Adversarial Examples ◽

Adversarial Example

Deep learning technology has been used to develop improved license plate recognition (LPR) systems. In particular, deep neural networks have brought significant improvements in the LPR system. However, deep neural networks are vulnerable to adversarial examples. In the existing LPR system, adversarial examples study specific spots that are easily identifiable by humans or require human feedback. In this paper, we propose a method of generating adversarial examples in the license plate, which has no human feedback and is difficult to identify by humans. In the proposed method, adversarial noise is added only to the license plate among the entire image to create an adversarial example that is erroneously recognized by the LPR system without being identified by humans. Experiments were performed using the baza silka dataset, and TensorFlow was used as the machine learning library. When epsilon is 0.6 for the first type, and alpha and the iteration of the second type are 0.4 and 1000, respectively, the adversarial examples generated by the first and second type generation methods are reduced to 20% and 15% accuracy in the LPR system.

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