Toward Deep-Learning-Based Methods in Image Forgery Detection: A Survey

Pham, Nam; Park, Chun-Su

doi:10.1109/access.2023.3241837

Cited by 11 publications

(4 citation statements)

References 107 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…LITERATURE REVIEW NAM THANH PHAM et al [1] Provides a thorough analysis of state-of-the-art DL-based photo imitation finding approaches. Two of the most well-known types of created images were considered: grafted photos and duplicate move photos.…”

Section: IImentioning

confidence: 99%

Adversarial Learning for Constrained Image Splicing Detection and Localization

Ahir,

Kapse,

Mhaske

et al. 2024

IJRASET

View full text Add to dashboard Cite

In the era of digital media, the manipulation of images has become a significant concern, particularly on social media platforms. Adversarial Learning for Constrained Image Splicing Detection and Localization aims to develop a robust system capable of recognizing and localizing spliced or tampered images. This research proposes a novel approach that leverages Convolutional Neural Networks (CNNs) and adversarial learningtechniques to enhance the detection and localization of image splicing. The system is designed to aid in distinguishing between authentic and manipulated images, thereby promoting transparency and trust in digital media. With the rapid advancement of digital media and image editingtools, the manipulation of visual content has become increasingly prevalent, posing significant challenges to the credibility and trustworthiness of images shared on social media platforms. One pervasive form of image manipulation is image splicing, where portions of two or more images are seamlessly combined to create a composite image, often with the intent to mislead or deceive viewers. Adversarial Learning for Constrained Image Splicing Detection and Localization aims to address this issue by developing a robust system capable of accurately detecting and localizing spliced regions within tampered images. This research proposes a novel approach that synergistically integrates Convolutional Neural Networks (CNNs) and adversarial learning techniques to enhance the performance of image splicing detection and localization. The proposed system employs a two-stage process: first, a CNN-based classifier is trained on a large dataset of authentic and spliced images to learn discriminative features for distinguishing between the two classes; subsequently, an adversarial learning model is employed to generate adversarial examples that can deceive the CNN classifier, while the classifier is iteratively updated to become more robust against these adversarial perturbations. By accurately identifying and localizing spliced regions within images, this research contributes to promoting transparency and trust in digital media shared on social media platforms, ultimately empowering users to make informed decisions about the authenticity of visual content. The proposed system has potential applications in various domains, including journalism, law enforcement, and content moderation, where verifying the integrity of visual evidence is of paramount importance.

show abstract

Section: IImentioning

confidence: 99%

Adversarial Learning for Constrained Image Splicing Detection and Localization

Ahir,

Kapse,

Mhaske

et al. 2024

IJRASET

View full text Add to dashboard Cite

show abstract

“…Pre-trained models and advanced algorithms improve accuracy and efficiency, creating a new benchmark in digital picture manipulation detection. As technology advances, these approaches will be developed, strengthening digital imaging fidelity in numerous sectors [13]. A different work utilized a CNN pre-trained on labelled pictures to extract features and train an SVM model [14].…”

Section: Related Workmentioning

confidence: 99%

Detecting Digital Image Forgeries with Copy-Move and Splicing Image Analysis using Deep Learning Techniques

Timothy,

Santra

2024

ijacsa

View full text Add to dashboard Cite

The proliferation of digitally altered images across social media platforms has escalated the urgency for robust image forgery detection systems. Traditional detection methodologies, while varied, often fall short in addressing the multifaceted nature of image forgeries in the digital landscape. Recognizing the need for advanced solutions, this paper introduces a novel deep-learning approach that leverages the architectural strengths of GNNs, CNNs, VGG16, MobileNet, and ResNet50. Our method uniquely integrates these architectures to effectively detect and analyze multiple types of image forgeries, including image splicing and copy-move forgeries. This approach is groundbreaking as it adapts these networks to focus on identifying discrepancies in the compression quality between forged and original image regions. By examining the differences between the original and compressed image versions, our model constructs a feature-rich representation, which is then analyzed by a tailored deep-learning network. This network has been enhanced by removing its original classifier and implementing a new one specifically designed for binary forgery classification. Very few researchers have explored the application of deep learning techniques in copy-move and splice image analysis for detecting digital image forgeries, making our work particularly significant. A comprehensive comparative analysis with pre-trained models underscores the superiority of our method, with the GNN model achieving an impressive accuracy of 98.54 percent on the CASIA V1 dataset. This not only sets a new benchmark in the field but also highlights the efficiency of our model, which benefits from reduced training parameters and accelerated training times.

show abstract

“…In the following sub-chapter, image forgery detection mechanisms are presented, with a deep focus on detecting only forgeries generated via the inpainting process (either manually or automatically). Regarding the overall forgery detection mechanism, there are existing reviews in this area, and a few that are relevant and have gone into a lot of detail are as follows: [3,5,[51][52][53]. Their focus is on generical forgery determination methods, while in the following paragraphs, the focus will be closer to the proposed methods for inpainting determination.…”

Section: Inpainting Forgery Detection Mechanismmentioning

confidence: 99%

Image Inpainting Forgery Detection: A Review

Barglazan,

Brad,

Constantinescu

2024

J. Imaging

View full text Add to dashboard Cite

In recent years, significant advancements in the field of machine learning have influenced the domain of image restoration. While these technological advancements present prospects for improving the quality of images, they also present difficulties, particularly the proliferation of manipulated or counterfeit multimedia information on the internet. The objective of this paper is to provide a comprehensive review of existing inpainting algorithms and forgery detections, with a specific emphasis on techniques that are designed for the purpose of removing objects from digital images. In this study, we will examine various techniques encompassing conventional texture synthesis methods as well as those based on neural networks. Furthermore, we will present the artifacts frequently introduced by the inpainting procedure and assess the state-of-the-art technology for detecting such modifications. Lastly, we shall look at the available datasets and how the methods compare with each other. Having covered all the above, the outcome of this study is to provide a comprehensive perspective on the abilities and constraints of detecting object removal via the inpainting procedure in images.

show abstract

Toward Deep-Learning-Based Methods in Image Forgery Detection: A Survey

Cited by 11 publications

References 107 publications

Adversarial Learning for Constrained Image Splicing Detection and Localization

Adversarial Learning for Constrained Image Splicing Detection and Localization

Detecting Digital Image Forgeries with Copy-Move and Splicing Image Analysis using Deep Learning Techniques

Image Inpainting Forgery Detection: A Review

Contact Info

Product

Resources

About