A New Fragile Watermarking Based on Chaos

2013 ◽  
Vol 401-403 ◽  
pp. 1772-1775
Author(s):  
Hui Fen Huang
Keyword(s):  
Good Practice ◽  
Chaotic Sequence ◽  
Fragile Watermarking ◽  
Image Feature ◽  
Original Image ◽  
Content Authentication ◽  
Tamper Localization ◽  
Image Block ◽  
Positioning Accuracy ◽  
Security Issues

For existing airspace fragile authentication watermarking algorithm positioning accuracy and security issues, this paper presents a fully-fragile watermarking based on chaos for the integrity of the image content authentication and tamper localization. The algorithm uses the original image block, the calculation of each pixel in the image block high-bit gray value as the image feature watermark information. The chaotic sequence is encrypted and determines the position of the watermark bit is embedded watermark information, tampering with the positioning accuracy of an image block of 2 × 2 pixels. Experimental results show that the algorithm is simple, safe, with good practice.

scholarly journals Intelligent Fault Diagnosis and Forecast of Time-Varying Bearing Based on Deep Learning VMD-DenseNet

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7467
Author(s):  
Shih-Lin Lin
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Image Feature ◽  
Signal Acquisition ◽  
Time Varying ◽  
Intelligent Fault Diagnosis ◽  
Image Block ◽  
Wide Range

Rolling bearings are important in rotating machinery and equipment. This research proposes variational mode decomposition (VMD)-DenseNet to diagnose faults in bearings. The research feature involves analyzing the Hilbert spectrum through VMD whereby the vibration signal is converted into an image. Healthy and various faults show different characteristics on the image, thus there is no need to select features. Coupled with the lightweight network, DenseNet, for image classification and prediction. DenseNet is used to build a model of motor fault diagnosis; its structure is simple, and the calculation speed is fast. The method of using DenseNet for image feature learning can perform feature extraction on each image block of the image, providing full play to the advantages of deep learning to obtain accurate results. This research method is verified by the data of the time-varying bearing experimental device at the University of Ottawa. Through the four links of signal acquisition, feature extraction, fault identification, and prediction, a mechanical intelligent fault diagnosis system has established the state of bearing. The experimental results show that the method can accurately identify four common motor faults, with a VMD-DenseNet prediction accuracy rate of 92%. It provides a more effective method for bearing fault diagnosis and has a wide range of application prospects in fault diagnosis engineering. In the future, online and timely diagnosis can be achieved for intelligent fault diagnosis.

An Efficient Blind Fragile Watermarking Scheme for Tamper Localization

2021 ◽  
pp. 749-757
Author(s):  
Shambhu Shankar Bharti ◽  
Shivendra Shivani ◽  
Sudhir Kumar Pandey ◽  
Suneeta Agarwal
Keyword(s):  
Fragile Watermarking ◽  
Tamper Localization ◽  
Watermarking Scheme

QUANTIZATION-BASED FRAGILE WATERMARKING USING BLOCK-WISE AUTHENTICATION AND PIXEL-WISE RECOVERY SCHEME FOR TAMPERED IMAGE

2013 ◽  
Vol 13 (02) ◽  
pp. 1340002 ◽  
Author(s):  
DURGESH SINGH ◽  
SHIVENDRA SHIVANI ◽  
SUNEETA AGARWAL
Keyword(s):  
Fragile Watermarking ◽  
Content Authentication ◽  
Image Content ◽  
Recovery Scheme ◽  
Tamper Recovery ◽  
Tampered Image ◽  
Watermarking Scheme ◽  
Content Recovery ◽  
Least Significant Bits

This paper suggests an efficient fragile watermarking scheme for image content authentication along with altered region restoration capability. In this scheme, image is divided into nonoverlapping blocks of size 2 × 2 and for each block, eight bits for image content recovery data and four bits for authentication data from five most significant bits (MSBs) of each pixel, are generated. These 12 bits are embedded into the least significant bits (LSBs) of the pixels which are placed in its corresponding mapping block. At the receiver end by comparing the recalculated and extracted authentication data, the tampered blocks can easily be identified and using recovery data, one can easily restore the tampered block. Results of experiments demonstrate that the proposed scheme is effective enough for alteration detection as well as tamper recovery of the image.

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