WITHDRAWN: Enhanced block based copy paste image forgery detection

Venu, K.; Sujatha, B.

doi:10.1016/j.matpr.2021.01.189

Cited by 2 publications

(2 citation statements)

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“…Their proposed method was efficient in detecting forged parts. Moreover, the block-based technique proved its high speed and efficiency in the literature and many works showed that such as [25][26][27] [28].…”

Section: Related Workmentioning

confidence: 99%

“…The Gabor filter was used to check the spatial and frequency domains aiming to detect the duplicated blocks. The Gabor filter is widely used to analyze the texture of images such studies are [23][24] [25]. The Gabor filter can be formalized as follows: 𝐺 𝑔 (𝑎, 𝑏) = ∑ 𝑁 0 𝐼(𝑎 − 𝑠, 𝑏 − 𝑡) × ℎ 𝑔 (𝑠, 𝑡)…”

Section: Feature Extractionmentioning

confidence: 99%

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Copy-Move Forgery Detection Using Texture Features of Hidden Forged Regions

Saeed¹,

Hamid²,

Hassan³

2023

Technium

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The recent revolution in technology has not only eased our daily activities at work and home but also introduced new threats. In their daily activities, people exchange a lot of files such as text files, images, videos, etc. that can be used for a variety of purposes. One of the most common types of files is images. These kinds of files can be used to socialize people or spread knowledge among communities. Some of the exchanged images are fake or forged which can lead to the spread of misinformation, which is dangerous. This paper tries to suggest a method for image forgery detection that is copy-move-based. This means a part of the image is used to hide or change other parts in the same image. The suggested method divides an image into several blocks. The feature vectors of the blocks are extracted using a modified Gabor filter. The extracted features are, then, reduced using the principal component analysis technique. The next step is to match the blocks and extract similar ones (duplicated blocks). The findings show that the suggested method is efficient compared to other methods in the literature in terms of detection rate and false positive detection. Also, the proposed method detected forged regions of images when having a 60% of compression rate.

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Section: Related Workmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%