Fake Image Detection Using Ensemble Learning

Singh, Divyasha; Jain, Trapti; Gupta, Neeta; Tolani, Bhavishya; Seeja, K. R.

doi:10.1007/978-981-99-3432-4_30

Cited by 1 publication

(1 citation statement)

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“…It involves the combination of the outputs of multiple individual models to create a more reliable and high-performing meta model. The key idea behind ensemble learning is that by aggregating the wisdom of several models, we can reduce the risk of overfitting and capture complex patterns in the data; this method was previously use in [18][19][20][21] for image forensics.…”

Section: Related Workmentioning

confidence: 99%

General Image Manipulation Detection Using Feature Engineering and a Deep Feed-Forward Neural Network

Ahmed,

Yoon,

Sharma

et al. 2023

Mathematics

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Within digital forensics, a notable emphasis is placed on the detection of the application of fundamental image-editing operators, including but not limited to median filters, average filters, contrast enhancement, resampling, and various other operations closely associated with these techniques. When conducting a historical analysis of an image that has potentially undergone various modifications in the past, it is a logical initial approach to search for alterations made by fundamental operators. This paper presents the development of a deep-learning-based system designed for the purpose of detecting fundamental manipulation operations. The research involved training a multilayer perceptron using a feature set of 36 dimensions derived from the gray-level co-occurrence matrix, gray-level run-length matrix, and normalized streak area. The system detected median filtering, mean filtering, the introduction of additive white Gaussian noise, and the application of JPEG compression in digital Images. Our system, which utilizes a multilayer perceptron trained with a 36-feature set, achieved an accuracy of 99.46% and outperformed state-of-the-art deep-learning-based solutions, which achieved an accuracy of 97.89%.

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Section: Related Workmentioning

confidence: 99%