Abstract

In recent years, more and more data has been created in digital form, allowing for easier control over storage and manipulation thanks to technological advancements. Unfortunately, these advancements also bring with them many risks, especially those related to the security of digital files. One of the concerns of many organisations is digital forgery, as it is increasingly easy to create fake images without leaving obvious traces of manipulation. One form of image forgery known as 'copy-move' is considered one of the most difficult problems in forgery detection. In this case, a portion of an image is copied and pasted at another location in the same image to hide unwanted objects in the scene. In this paper, we propose a method that automatically detects duplication areas within the same image. Duplication detection is performed by identifying local characteristics of the image (key points) using the Scale Invariant Feature Transform (SIFT) method and matching identical features using the Singular Value Decomposition (SVD) method. The results obtained show that our proposed hybrid method is robust to geometric transformations and is able to detect duplication areas with high performance.

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