Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
Image tampering is a growing concern
in numerous fields, necessitating robust solutions. This study investigates the
creation of an optimal strategy to resolve the vulnerability of image tampering
(manipulation). Beginning with a survey of contemporary alteration detection
techniques, their strengths and limitations in identifying manipulated image
regions are evaluated. The complexity of both global and local manipulation is
highlighted, highlighting the need for multifaceted analysis. Combining
conventional image forensics techniques with advanced machine learning
algorithms, the devised strategy forms a comprehensive framework. This
synthesis seeks to produce a robust and adaptable method capable of detecting
corruption even in the presence of sophisticated manipulation techniques. The
significance of a diverse training dataset is highlighted, lending credibility
to the evaluation of the strategy. Real-world interference scenarios and
diverse image formats enhance its dependability and generalization
capabilities. Ethical considerations are interwoven to ensure a balanced
approach that protects both the privacy rights of individuals and the
authenticity of images. The paper concludes with empirical evidence
demonstrating the effectiveness of the proposed strategy. Comparisons with
extant techniques highlight its prowess, revealing improvements in precision,
efficiency, and resiliency. The road ahead entails continuous improvement via
learning mechanisms and adaptation to oppose emergent tampering methods. This
research represents a significant advance in the field of image forensics. It
strengthens digital image security, authenticity, and trustworthiness by
presenting the optimal strategy. In turn, this enables more informed
decision-making across various sectors, paving the way for a more reliable
digital landscape.
Country : Sri Lanka
IRJIET, Volume 7, Issue 11, November 2023 pp. 511-519