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PIH-mSCM: a modified spiking cortical model for perceptual image hashing and its application to copy detection

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Abstract

For one of the most crucial copyright protection of multimedia content, copy image detection necessitates feature extraction and feature matching. However, matching features necessitate extra computation and storage time, which limits its flexibility. Recent research shows the efficiency of perceptual image hashing in the said case. Consequently, we propose a system that uses the spiking cortical model (SCM) as its foundation and modifies it to create a modified SCM (mSCM) to construct the output time-series waveform. The model is iterated to produce a stable feature vector, which is then used to generate hashes. Excellent resistance against geometric distortions and improved discrimination are provided by the SCM feature vector. Additionally, illustrative experimental findings demonstrate the better stability, resilience, and discrimination of our approach, which enables better copy detection than the state-of-the-art techniques.

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Data Availability

The datasets used to support the findings of this study are available from the public repositories mentioned in the manuscript.

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Authors and Affiliations

  1. Computer Vision Laboratory, Department of Computer Science and Engineering, National Institute of Technology Silchar, Silchar, 788010, Assam, India

    Moumita Roy, Dalton Meitei Thounaojam & Shyamosree Pal

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  1. Moumita Roy
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  2. Dalton Meitei Thounaojam
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  3. Shyamosree Pal
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Correspondence to Moumita Roy.

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Dalton Meitei Thounaojam and Shyamosree Pal are contributed equally to this work.

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Roy, M., Thounaojam, D.M. & Pal, S. PIH-mSCM: a modified spiking cortical model for perceptual image hashing and its application to copy detection. Multimed Tools Appl 83, 38291–38312 (2024). https://doi.org/10.1007/s11042-023-16753-4

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  • DOI: https://doi.org/10.1007/s11042-023-16753-4

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