Detecting Fake Image: A Review for Stopping Image Manipulation

Lubna, Jahanara Islam; Chowdhury, Sanjoy

doi:10.1007/978-981-15-3666-3_13

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…Manipulation can be reliably detected by assessing the integrity of the inserted structure. However, this approach requires that the structure be inserted correctly in a trustworthy image capture before any manipulation, as these methods only enable the detection of modifications post-insertion [12].…”

Section: Introductionmentioning

confidence: 99%

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Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks

Alencar,

Lopes,

Fernandes

et al. 2024

Future Internet

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In the current era of social media, the proliferation of images sourced from unreliable origins underscores the pressing need for robust methods to detect forged content, particularly amidst the rapid evolution of image manipulation technologies. Existing literature delineates two primary approaches to image manipulation detection: active and passive. Active techniques intervene preemptively, embedding structures into images to facilitate subsequent authenticity verification, whereas passive methods analyze image content for traces of manipulation. This study presents a novel solution to image manipulation detection by leveraging a multi-stream neural network architecture. Our approach harnesses three convolutional neural networks (CNNs) operating on distinct data streams extracted from the original image. We have developed a solution based on two passive detection methodologies. The system utilizes two separate streams to extract specific data subsets, while a third stream processes the unaltered image. Each net independently processes its respective data stream, capturing diverse facets of the image. The outputs from these nets are then fused through concatenation to ascertain whether the image has undergone manipulation, yielding a comprehensive detection framework surpassing the efficacy of its constituent methods. Our work introduces a unique dataset derived from the fusion of four publicly available datasets, featuring organically manipulated images that closely resemble real-world scenarios. This dataset offers a more authentic representation than other state-of-the-art methods that use algorithmically generated datasets based on image patches. By encompassing genuine manipulation scenarios, our dataset enhances the model’s ability to generalize across varied manipulation techniques, thereby improving its performance in real-world settings. After training, the merged approach obtained an accuracy of 89.59% in the set of validation images, significantly higher than the model trained with only unaltered images, which obtained 78.64%, and the two other models trained using images with a feature selection method applied to enhance inconsistencies that obtained 68.02% for Error-Level Analysis images and 50.70% for the method using Discrete Wavelet Transform. Moreover, our proposed approach exhibits reduced accuracy variance compared to alternative models, underscoring its stability and robustness across diverse datasets. The approach outlined in this work needs to provide information about the specific location or type of tempering, which limits its practical applications.

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Section: Introductionmentioning

confidence: 99%

“…To detect image forgery, passive methods can either analyze artifacts intrinsic to a digital image or search for inconsistencies in the content of an image. According to Lubna and Chowdhury [12], the artifacts present in digital images can be divided into three types: acquisition, format, and manipulation.…”

Section: Introductionmentioning

confidence: 99%

Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks

Alencar,

Lopes,

Fernandes

et al. 2024

Future Internet

View full text Add to dashboard Cite

show abstract

Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks

Alencar,

Lopes,

Fernandes

et al. 2024

Preprint

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In the age of social media, images come from unreliable sources that may change their content to fit a narrative. Automated detection of forged images is a complex task, especially considering recent technological advances in image manipulation software. However, in the existing literature, it is possible to identify two general approaches adopted by detection methods, active and passive. Active techniques preemptively act on an image by inserting structures before any manipulation is made that can verify its authenticity. In contrast, passive methods analyze the content of an image in search of traces of manipulation. In this way, this research proposes a novel approach to image manipulation detection, combining two passive methods through neural networks, creating a generalist approach capable of detecting with greater accuracy than the methods that compose it. Furthermore, this work used a combination of four datasets available in the literature for training and evaluation. After training, the merged approach obtained an accuracy of 89.59% in the set of validation images, significantly higher than the model trained with only unaltered images, which obtained 78.64%, and the two other models trained using images with a feature selection method applied to enhance inconsistencies that obtained 68.02% for Error-Level Analysis images and 50.70% for the method using Discrete Wavelet Transform. In addition to the performance improvement, the proposed approach’s accuracy variation was lower than the other models.

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Tracing Origin of Social Media Posts

TT¹,

Anupama²,

Bharathi³

et al. 2022

IJARSCT

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This problem statement should result in a solution that can identify perpetrators who first posted sexually explicit abuse content on social media platforms. Images and videos of individuals of a sexually explicit nature are quite often posted online intentionally by perpetrators on various social media platforms with the sole intent of causing harassment, humiliation and distress to the victim. Social media platforms are constantly trying to create and improve automatic mechanisms to identify and reject such content at the time of upload itself. However, there are times when such content containing nudity and of a sexually explicit nature, fails to be detected at the time of upload and gets published. Such content usually has a tendency to instantly become viral, and others thereafter share it, or download and post and it again from their social media accounts. Social media platforms take down such posts when reported but by the time this is done, several copies have already been made and go in circulation. It quickly becomes ambiguous as to who posted it first, and taking advantage of this ambiguity, the original perpetrator evades detection. It is therefore critical to identify the person who first posted such distressing content online. Given a piece of text, image or video snippet as input, build a solution that can identify the person who was the first one to post it online on a particular social media platform. Please bear in mind that people could have copied it and made minor modifications before re-posting it from their accounts. Participants are expected to obtain suitable data required to work on this problem statement on their own.

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Detecting Fake Image: A Review for Stopping Image Manipulation

Cited by 3 publications

References 30 publications

Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks

Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks

Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks

Tracing Origin of Social Media Posts

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