Image Forgery Detection - Deep Learning Approaches: Studying deep learning approaches for detecting image forgeries and manipulations, including techniques for identifying digital tampering
Keywords:
Image forgery detection, deep learningAbstract
Image forgery and manipulation have become prevalent with the rise of digital media, leading to challenges in verifying the authenticity of images. This paper presents a comprehensive study of deep learning approaches for detecting image forgeries, focusing on techniques for identifying digital tampering. We review recent advancements in deep learning models, datasets, and evaluation metrics for image forgery detection. Additionally, we discuss the limitations of existing approaches and propose future research directions to enhance the robustness of image forgery detection systems.
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K. Joel Prabhod, “ASSESSING THE ROLE OF MACHINE LEARNING AND COMPUTER VISION IN IMAGE PROCESSING,” International Journal of Innovative Research in Technology, vol. 8, no. 3, pp. 195–199, Aug. 2021, [Online]. Available: https://ijirt.org/Article?manuscript=152346
Sadhu, Ashok Kumar Reddy. "Reimagining Digital Identity Management: A Critical Review of Blockchain-Based Identity and Access Management (IAM) Systems-Architectures, Security Mechanisms, and Industry-Specific Applications." Advances in Deep Learning Techniques 1.2 (2021): 1-22.
Tatineni, Sumanth, and Anjali Rodwal. “Leveraging AI for Seamless Integration of DevOps and MLOps: Techniques for Automated Testing, Continuous Delivery, and Model Governance”. Journal of Machine Learning in Pharmaceutical Research, vol. 2, no. 2, Sept. 2022, pp. 9-41, https://pharmapub.org/index.php/jmlpr/article/view/17.
Sadhu, Ashok Kumar Reddy, and Amith Kumar Reddy. "Exploiting the Power of Machine Learning for Proactive Anomaly Detection and Threat Mitigation in the Burgeoning Landscape of Internet of Things (IoT) Networks." Distributed Learning and Broad Applications in Scientific Research 4 (2018): 30-58.
Tatineni, Sumanth, and Venkat Raviteja Boppana. "AI-Powered DevOps and MLOps Frameworks: Enhancing Collaboration, Automation, and Scalability in Machine Learning Pipelines." Journal of Artificial Intelligence Research and Applications 1.2 (2021): 58-88.
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