METHOD FOR DETECTING IMAGE FORGERY

Abstract

ABSTRACT A method (100) for detecting image forgery, the method (100) comprising the steps of acquiring a dataset of authentic and manipulated images, pre-processing the dataset by resizing, normalizing, and performing noise reduction, employing a convolutional neural network (CNN) model architecture to extract spatial features from the dataset, training the CNN model architecture on a subset of the dataset to enable identification of various types of forgery manipulations and validating the CNN model architecture to achieve high detection accuracy. <>

Specification

Description:METHOD FOR FOR DETECTING IMAGE FORGERY

FIELD OF THE DISCLOSURE
[0001] This invention generally relates to a field of image forgery and in particular relates to a method for detecting image forgery.
BACKGROUND

[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
[0003] Image forgery, or the manipulation of digital images, is a growing concern in fields ranging from digital forensics to journalism. With advancements in image editing tools, creating forgeries that are indistinguishable from authentic images has become increasingly eas , Claims:1. A method (100) for detecting image forgery, the method (100) comprising the steps of:
acquiring a dataset of authentic and manipulated images;
pre-processing the dataset by resizing, normalizing, and performing noise reduction;
employing a convolutional neural network (CNN) model architecture to extract spatial features from the dataset;
training the CNN model architecture on a subset of the dataset to enable identification of various types of forgery manipulations; and
validating the CNN model architecture to achieve high detection accuracy.

2. The method (100) as claimed in claim 1, wherein the convolutional neural network (CNN) model architecture includes convolutional and pooling layers.

3. The method (100) as claimed in claim 1, further comprising displaying the forgery detection results on a user-friendly interface that allows users to upload images, adjust parameters of CNN model architecture, and visualize detection results, wherein the user interface highlights regions of the one or more ima

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