A JPEG compression-resistant data watermark embedding and detection algorithm

2021 ◽  
Author(s):  
Pengfei Yu ◽  
Xiuli Huang ◽  
Bo Hu ◽  
Xiaoming Zhou
Keyword(s):  
Detection Algorithm ◽  
Jpeg Compression ◽  
Watermark Embedding ◽  
Jpeg Compression Resistant

A Reversible Watermarking Algorithm Resistant to Image Geometric Transformation

2019 ◽  
Vol 11 (1) ◽  
pp. 100-113
Author(s):  
Jian Li ◽  
Jinwei Wang ◽  
Shuang Yu ◽  
Xiangyang Luo
Keyword(s):  
High Performance ◽  
Side Information ◽  
Jpeg Compression ◽  
Reversible Watermarking ◽  
Experimental Results ◽  
Geometric Transformation ◽  
Original Image ◽  
Robust Watermark ◽  
Watermark Embedding ◽  
Watermarking Scheme

This article proposes a novel robust reversible watermarking algorithm. The proposed watermarking scheme is reversible because the original image can be recovered after extracting watermarks from the watermarked image, as long as it is not processed by an attacker. The scheme is robust because watermarks can still be extracted from watermarked images, even if it is undergone some malicious or normal operations like rotation and JPEG compression. It first selects two circles, which are centred at the centroid and the centre of image. Then, statistic quantities of these two circles are employed for robust watermark embedding by altering the pixels' value. The side information generated by above embedding process will be embedded as fragile watermarks at another stage to ensure the recovery of original image. Experimental results verify the high performance of the proposed algorithm in resisting various attacks, including JPEG compression and geometric transformation.

scholarly journals A lightweight buyer-seller watermarking protocol based on time-stamping and composite signal representation

2018 ◽  
Vol 7 (4.6) ◽  
pp. 39 ◽  
Author(s):  
Ashwani Kumar ◽  
Paras Jain ◽  
Jabir Ali ◽  
Shrawan Kumar ◽  
John Samuel Babu
Keyword(s):  
Digital Watermarking ◽  
Detection Algorithm ◽  
Digital Content ◽  
Content Authentication ◽  
Signal Representation ◽  
Content Provider ◽  
Signal Representations ◽  
Watermark Embedding ◽  
Watermarking Scheme ◽  
Composite Signal

The protocol allows a content provider to detect duplicate copy of a digital content and restrict the content provider who blames the innocent customer. This paper, proposed a lightweight protocol, which uses composite signal representation and time-stamping for watermark embedding and extraction. We have used timestamp, which tells at what time the digital content was created, signed or verified to digital watermarking algorithms and uses the composite signal representation for minimizing the overhead and bandwidth due to the use of composite signals. The suggested protocol uses composite signal representations and timestamp based methods with digital watermarking scheme for content authentication. Our watermark embedding and detection   algorithm achieves a balance between robustness and image visual quality.     Simulation results demonstrate that the algorithm used by proposed protocol has an increase robustness and good quality of watermark images as well and withstand against various image-processing attacks. 

IMAGE WATERMARKING BASED ON THE HESSENBERG TRANSFORM

2009 ◽  
Vol 09 (03) ◽  
pp. 411-433 ◽  
Author(s):  
HASSEN SEDDIK ◽  
MOUNIR SAYADI ◽  
FARHAT FNAIECH ◽  
MOHAMED CHERIET
Keyword(s):  
Spatial Frequency ◽  
Copyright Protection ◽  
Image Watermarking ◽  
Image Representation ◽  
Jpeg Compression ◽  
New Technique ◽  
Detection Process ◽  
Data Owner ◽  
Watermark Embedding ◽  
Wide Range

Watermarking is now considered as an efficient means for assuring copyright protection and data owner identification. Watermark embedding techniques depend on the representation domain of the image (spatial, frequency, and multiresolution). Every domain has its specific advantages and limitations. Moreover, each technique in a chosen domain is found to be robust to specific sets of attack types. So we need to propose more robust domains to defeat these limitations and respect all the watermarking criterions (capacity, invisibility and robustness). In this paper, a new watermarking method is presented using a new domain for the image representation and the watermark embedding: the mathematical Hessenberg transformation. This domain is found to be robust against a wide range of STIRMARK attacks such as JPEG compression, convolution filtering and noise adding. The robustness of the new technique in preserving and extracting the embedded watermark is proved after various attacks types. It is also improved when compared with other methods in use. In addition, the proposed method is blind and the use of the host image is not needed in the watermark detection process.

A Review on Watermarking using Local Binary Pattern

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Sanket Kumar Srivastava ◽  
Prabha Kant Dwivedi
Keyword(s):  
Digital Watermarking ◽  
Local Binary Pattern ◽  
Local Binary Patterns ◽  
Jpeg Compression ◽  
Sensor Data ◽  
Multimedia Content ◽  
Host Image ◽  
Watermark Embedding ◽  
Security Problem ◽  
The Given

Local binary patterns are best known because of their robust texture-defining capacities and digital watermarking used to prove multimedia content copyright. This work presents an overview of the binary watermark in the image blocks by changing the pixels conveyed by the LBP pattern of the neighborhood. However, different photo blocks can have the same LBP pattern, which in the watermark process can lead to incorrect detection. In other words, without changing your watermark message, one can change the host image deliberately. Moreover, before watermark embedding, there is no encryption procedure, which leads to another potential security problem. In this paper, we examine the identical process of LBP synthesis or reverse LBP and its suitability for the digital watermarking image. The process of LBP synthesis varies by pixel values so that the LBP from these pixels is the required synthesizable value. Due to the LBP synthesis character, the watermark needs to be integrated with only a few pixels of the given block. The results show that rotational, JPEG compression, and scalable attacks are robust with the technique. This LBP synthesis could also be used to justify ownership using watermark sensor data.

Read, Understand, Learn, & Excel: Development and Testing of an Automated Reading Strategy Detection Algorithm for Postsecondary Students

2019 ◽  
Vol 28 (3) ◽  
pp. 1257-1267 ◽  
Author(s):  
Priya Kucheria ◽  
McKay Moore Sohlberg ◽  
Jason Prideaux ◽  
Stephen Fickas
Keyword(s):  
Academic Success ◽  
Undergraduate Students ◽  
Expository Text ◽  
Reading Strategy ◽  
Detection Algorithm ◽  
Expository Texts ◽  
Human Observer ◽  
Behavioral Strategies ◽  
Periodic Review ◽  
Wide Range

PurposeAn important predictor of postsecondary academic success is an individual's reading comprehension skills. Postsecondary readers apply a wide range of behavioral strategies to process text for learning purposes. Currently, no tools exist to detect a reader's use of strategies. The primary aim of this study was to develop Read, Understand, Learn, & Excel, an automated tool designed to detect reading strategy use and explore its accuracy in detecting strategies when students read digital, expository text.MethodAn iterative design was used to develop the computer algorithm for detecting 9 reading strategies. Twelve undergraduate students read 2 expository texts that were equated for length and complexity. A human observer documented the strategies employed by each reader, whereas the computer used digital sequences to detect the same strategies. Data were then coded and analyzed to determine agreement between the 2 sources of strategy detection (i.e., the computer and the observer).ResultsAgreement between the computer- and human-coded strategies was 75% or higher for 6 out of the 9 strategies. Only 3 out of the 9 strategies–previewing content, evaluating amount of remaining text, and periodic review and/or iterative summarizing–had less than 60% agreement.ConclusionRead, Understand, Learn, & Excel provides proof of concept that a reader's approach to engaging with academic text can be objectively and automatically captured. Clinical implications and suggestions to improve the sensitivity of the code are discussed.Supplemental Materialhttps://doi.org/10.23641/asha.8204786

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