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AI-Based Copyright Detection Of An Image In a Video Using Degree Of Similarity And Image Hashing

Ashutosh, Rahul Jashvantbhai Pandya

TL;DR

This work tackles the problem of identifying when a copyrighted image appears within video content by integrating multiple similarity measures. It combines ORB feature matching, SSIM perceptual similarity, and perceptual image hashing into a frame-wise detection pipeline, with frame extraction via OpenCV and a test image as reference. The authors present a workflow that computes per-frame similarity scores and uses hashing to categorize frames for dataset creation, reporting maximal observed similarities of $0.097$ (ORB) and $0.301$ (SSIM) to illustrate decision thresholds. A thresholding strategy and a path to a labeled training dataset are proposed to enable automated copyright detection in video streams. The approach offers a practical framework for automated monitoring and potential training data generation for ML-based copyright protection systems.

Abstract

The expanse of information available over the internet makes it difficult to identify whether a specific work is a replica or a duplication of a protected work, especially if we talk about visual representations. Strategies are planned to identify the utilization of the copyrighted image in a report. Still, we want to resolve the issue of involving a copyrighted image in a video and a calculation that could recognize the degree of similarity of the copyrighted picture utilized in the video, even for the pieces of the video that are not featured a lot and in the end perform characterization errands on those edges. Machine learning (ML) and artificial intelligence (AI) are vital to address this problem. Numerous associations have been creating different calculations to screen the identification of copyrighted work. This work means concentrating on those calculations, recognizing designs inside the information, and fabricating a more reasonable model for copyrighted image classification and detection. We have used different algorithms like- Image Processing, Convolutional Neural Networks (CNN), Image hashing, etc. Keywords- Copyright, Artificial Intelligence(AI), Copyrighted Image, Convolutional Neural Network(CNN), Image processing, Degree of similarity, Image Hashing.

AI-Based Copyright Detection Of An Image In a Video Using Degree Of Similarity And Image Hashing

TL;DR

This work tackles the problem of identifying when a copyrighted image appears within video content by integrating multiple similarity measures. It combines ORB feature matching, SSIM perceptual similarity, and perceptual image hashing into a frame-wise detection pipeline, with frame extraction via OpenCV and a test image as reference. The authors present a workflow that computes per-frame similarity scores and uses hashing to categorize frames for dataset creation, reporting maximal observed similarities of (ORB) and (SSIM) to illustrate decision thresholds. A thresholding strategy and a path to a labeled training dataset are proposed to enable automated copyright detection in video streams. The approach offers a practical framework for automated monitoring and potential training data generation for ML-based copyright protection systems.

Abstract

The expanse of information available over the internet makes it difficult to identify whether a specific work is a replica or a duplication of a protected work, especially if we talk about visual representations. Strategies are planned to identify the utilization of the copyrighted image in a report. Still, we want to resolve the issue of involving a copyrighted image in a video and a calculation that could recognize the degree of similarity of the copyrighted picture utilized in the video, even for the pieces of the video that are not featured a lot and in the end perform characterization errands on those edges. Machine learning (ML) and artificial intelligence (AI) are vital to address this problem. Numerous associations have been creating different calculations to screen the identification of copyrighted work. This work means concentrating on those calculations, recognizing designs inside the information, and fabricating a more reasonable model for copyrighted image classification and detection. We have used different algorithms like- Image Processing, Convolutional Neural Networks (CNN), Image hashing, etc. Keywords- Copyright, Artificial Intelligence(AI), Copyrighted Image, Convolutional Neural Network(CNN), Image processing, Degree of similarity, Image Hashing.
Paper Structure (12 sections, 8 equations, 13 figures)

This paper contains 12 sections, 8 equations, 13 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. ORB feature matching
  4. Structural Similarity(SSIM)
  5. Image Hashing
  6. Methodology
  7. Computation of degree of similarity
  8. ORB function
  9. SSIM functionSS
  10. Image Hashing
  11. SIMULATION RESULT
  12. Conclusion

Figures (13)

  • Figure 1: ORB feature matching algorithm
  • Figure 2: Structural Similarity (x and y inputs to be compared)
  • Figure 3: Image hashing
  • Figure 4: Working mechanism
  • Figure 5: ORB functionality
  • ...and 8 more figures