Digital image splicing detection based on Markov features in QDCT and QWT domain (1708.08245v3)

Abstract: Image splicing detection is of fundamental importance in digital forensics and therefore has attracted increasing attention recently. In this paper, a color image splicing detection approach is proposed based on Markov transition probability of quaternion component separation in quaternion discrete cosine transform (QDCT) domain and quaternion wavelet transform (QWT) domain. Firstly, Markov features of the intra-block and inter-block between block QDCT coefficients are obtained from the real part and three imaginary parts of QDCT coefficients respectively. Then, additional Markov features are extracted from luminance (Y) channel in quaternion wavelet transform domain to characterize the dependency of position among quaternion wavelet subband coefficients. Finally, ensemble classifier (EC) is exploited to classify the spliced and authentic color images. The experiment results demonstrate that the proposed approach can outperforms some state-of-the-art methods.

Overview of Research Limitations and Access in Scientific Publishing

The paper referenced by the identifier "(1708.08245)v3" presents an evident and recurring issue within the field of scientific communication and open access publishing: the availability of research outputs. Upon examining the available information, it is clear that the content of the paper itself is not accessible, as no PDF has been provided by the author for public viewing on the arXiv platform.

This situation underscores a significant limitation within open-access repositories, wherein despite the foundational intention to democratize access to scientific knowledge, technical and procedural barriers may impede full transparency and dissemination. Such an occurrence poses challenges both for individual researchers attempting to engage with specific papers and for the broader academic community striving for equitable access to scientific advancements.

Implications for Academic Research

• Accessibility: The lack of PDF availability for paper (1708.08245)v3 highlights an issue of accessibility that can inhibit research progress. Researchers unable to access detailed papers are hindered in their capabilities to build upon previous work, potentially reducing the pace of innovation and collaborative endeavors.
• Reliability of Open Access Platforms: While platforms like arXiv are instrumental in providing free access to a plethora of research publications, this instance demonstrates that their capacity to effectively serve the community is dependent on authors adhering to submission protocols. This emphasizes the need for more robust systems and guidelines that ensure research outputs are universally accessible once submitted.
• Scientific Discourse: Restricted access to certain papers may affect the level of discourse within specific fields, as researchers often rely on the dissemination of comprehensive studies to validate and question existing paradigms. Unavailability creates informational gaps that can skew the trajectory of scientific exploration and exchange.

Future Considerations in AI and Open Access

As developments in AI continue to shape academic research and publication processes, several considerations should be taken into account:

1. Automated Systems for Submission Verification: Implementing AI-driven solutions for the submission and verification process could enhance the reliability of public repositories by ensuring compliance and format accessibility from the onset.
2. Impact on Citation Metrics: Sufficient access to research allows proper citation and referencing, which are critical to an author's academic reputation. An inaccessible paper may suffer from reduced citations, not reflective of its potential impact, and automation could mitigate this issue.
3. Policy Development: Institutions and funding bodies must prioritize and foster policies that mandate accessibility compliance, leveraging AI to monitor adherence as part of operational status checks.

The case of paper (1708.08245)v3 serves as a reminder of existing limitations within open access models and inspires discussions on optimizing accessibility in scientific publishing, ensuring that future AI developments are aligned with these goals.