As the H.264/AVC-based video products become more and more popular, issues of copyright protection and authentication that are appropriate for this standard will be very important. In this paper, a content-based authentication watermarking scheme for H.264/AVC video is proposed. Considering the new feature of H.264/AVC, the content-based authentication code for spatial tampering is firstly generated using the reliable features extracted from video frame blocks. The authentication code, which can detect malicious manipulations but allow recompression, is embedded into the DCT coefficients in diagonal positions using a novel modulation method. Spatial tampering can be located by comparing the extracted and the original feature-based watermarks. In addition, combining ECC and interleaving coding, the frame index of each video frame is used as watermark information and embedded in the residual coefficients. Temporal tampering can be detected by the mismatch between the extracted and the observed frame index. Experimental results show that the proposed scheme can discriminate the malicious tampering from the mild signal processing. The tampered location can also be approximately determined according to the glide window and the predefined threshold.

A novel watermarking scheme for H.264/AVC video authentication

This paper introduces a digital video watermarking scheme for real-time authentication of H.264/AVC compressed videos. The watermark signals, which represent the macroblock's (MB) and frame's indices, are embedded into the last nonzero quantized DCT value of blocks. Also, analyzing the detection error helps to distinguish malicious attacks from some common processing such as H.264/AVC recompression. Experimental results show that the distortion caused by our watermarking system is negligible, leading to a PSNR of about -0.78 dB and increasing the bitrate by just 3.6%. Furthermore, average bit correct rate (BCR) after H.264/AVC recompression is 0.725.

A realtime spatio-temporal watermarking scheme for H.264/AVC

The paper put forward a semantic web retrieval model based on ontology according to the defects of lack of knowledge representation and semantic processing capability in the traditional keyword-based information retrieval, and describes the main function modules and the key technologies.

Design of Semantic Web Retrieval Model Based on Ontology

The information retrieval based on ontology is a hotspot in the domain of information retrieval According to the study on the existed retrieval model, this paper proposes a new kind of ontology-based semantic retrieval model, which grants semantic to the retrieval entry, the process of retrieval and the organization of data and consequently improves the precision and recall of information retrieval

Study on ontology-based Semantic Retrieval Model

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Yi Zheng

Author Tools

Chat about Author

Papers

Study on ontology-based Semantic Retrieval Model