Robust forgery detection for compressed images using CNN supervision

Diallo, Boubacar; Urruty, Thierry; Bourdon, Pascal; Fernández-Maloigne, Christine

doi:10.1016/j.fsir.2020.100112

Cited by 40 publications

(16 citation statements)

References 18 publications

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“…Diallo et al [ 11 ] introduce an architecture enhancing strength for image counterfeit recognition. The vital stage of this architecture is to consider the image quality matching to the selected application.…”

Section: Related Workmentioning

confidence: 99%

Design of Automated Deep Learning-Based Fusion Model for Copy-Move Image Forgery Detection

Krishnaraj

Sivakumar

Kuppusamy³

et al. 2022

Computational Intelligence and Neuroscience

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Due to the exponential growth of high-quality fake photos on social media and the Internet, it is critical to develop robust forgery detection tools. Traditional picture- and video-editing techniques include copying areas of the image, referred to as the copy-move approach. The standard image processing methods physically search for patterns relevant to the duplicated material, restricting the usage in enormous data categorization. On the contrary, while deep learning (DL) models have exhibited improved performance, they have significant generalization concerns because of their high reliance on training datasets and the requirement for good hyperparameter selection. With this in mind, this article provides an automated deep learning-based fusion model for detecting and localizing copy-move forgeries (DLFM-CMDFC). The proposed DLFM-CMDFC technique combines models of generative adversarial networks (GANs) and densely connected networks (DenseNets). The two outputs are combined in the DLFM-CMDFC technique to create a layer for encoding the input vectors with the initial layer of an extreme learning machine (ELM) classifier. Additionally, the ELM model’s weight and bias values are optimally adjusted using the artificial fish swarm algorithm (AFSA). The networks’ outputs are supplied into the merger unit as input. Finally, a faked image is used to identify the difference between the input and target areas. Two benchmark datasets are used to validate the proposed model’s performance. The experimental results established the proposed model’s superiority over recently developed approaches.

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Section: Related Workmentioning

confidence: 99%

Design of Automated Deep Learning-Based Fusion Model for Copy-Move Image Forgery Detection

Krishnaraj

Sivakumar

Kuppusamy³

et al. 2022

Computational Intelligence and Neuroscience

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“…One of the most impressive forms of ANN architecture is the Convolutional Neural Network (CNN) [ 36 ]. CNN is a deep learning algorithm which takes as input an input image, assigns importance (learning weights and biases) to various aspects and objects present in the image, and has the ability to differentiate one from the other.…”

Section: Literature Review and State Of The Artmentioning

confidence: 99%

Exposing Manipulated Photos and Videos in Digital Forensics Analysis

2021

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Tampered multimedia content is being increasingly used in a broad range of cybercrime activities. The spread of fake news, misinformation, digital kidnapping, and ransomware-related crimes are amongst the most recurrent crimes in which manipulated digital photos and videos are the perpetrating and disseminating medium. Criminal investigation has been challenged in applying machine learning techniques to automatically distinguish between fake and genuine seized photos and videos. Despite the pertinent need for manual validation, easy-to-use platforms for digital forensics are essential to automate and facilitate the detection of tampered content and to help criminal investigators with their work. This paper presents a machine learning Support Vector Machines (SVM) based method to distinguish between genuine and fake multimedia files, namely digital photos and videos, which may indicate the presence of deepfake content. The method was implemented in Python and integrated as new modules in the widely used digital forensics application Autopsy. The implemented approach extracts a set of simple features resulting from the application of a Discrete Fourier Transform (DFT) to digital photos and video frames. The model was evaluated with a large dataset of classified multimedia files containing both legitimate and fake photos and frames extracted from videos. Regarding deepfake detection in videos, the Celeb-DFv1 dataset was used, featuring 590 original videos collected from YouTube, and covering different subjects. The results obtained with the 5-fold cross-validation outperformed those SVM-based methods documented in the literature, by achieving an average F1-score of 99.53%, 79.55%, and 89.10%, respectively for photos, videos, and a mixture of both types of content. A benchmark with state-of-the-art methods was also done, by comparing the proposed SVM method with deep learning approaches, namely Convolutional Neural Networks (CNN). Despite CNN having outperformed the proposed DFT-SVM compound method, the competitiveness of the results attained by DFT-SVM and the substantially reduced processing time make it appropriate to be implemented and embedded into Autopsy modules, by predicting the level of fakeness calculated for each analyzed multimedia file.

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“…Liu et al [7] proposed rich model-based features [31] with calibrated neighboring joint density and a hybrid large feature mining approach to achieve state-of-the-art in terms of detection accuracy [32] . Deep learning is widely used for various areas [33][34][35][36][37][38][39] , it is also applied to detect image forgery [40][41][42] . For example, Yao et al [43] developed a reliability fusion map based CNN model to detect image forgery.…”

Section: Seam Carving and Relevant Image Forgery Detectionmentioning

confidence: 99%

“…The authors evaluated their model on the CASIA-v2 dataset. Diallo et al [41] developed a framework for deep learning detection of forged images. The researchers evaluated their proposed framework on the CMI dataset.…”

Section: Seam Carving and Relevant Image Forgery Detectionmentioning

confidence: 99%

A comparison study to detect seam carving forgery in JPEG images with deep learning models

Celebi¹,

Hsu²

2022

J Surveill Secur Saf

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Aim: Although deep learning has been applied in image forgery detection, to our knowledge, it still falls short of a comprehensive comparison study in detecting seam-carving images in multimedia forensics by comparing the popular deep learning models, which is addressed in this study. Methods: To investigate the performance in detecting seam-carving-based image forgery with popular deep learning models that were used in image forensics, we compared nine different deep learning models in detecting untouched JPEG images, seam-insertion images, and seam removal images (three-class classification), and in distinguishing modified seam-carving images from untouched JPEG images (binary classification). We also investigate the different learning algorithms with the Efficientnet-B5 in adjusting the learning rate with three popular optimizers in deep learning. Results: Our study shows that EfficientNet performs the best among the nine different deep learning frameworks, followed by SRnet, and LFnet. Different algorithms for adjusting the learning rate result in different detection testing accuracy with Efficientnet-B5. In our experiments, decouples the optimal choice of weight decay factor from the setting of the learning rate (AdamW) is generally superior to Adaptive Moment Estimation (Adam) and Stochastic Gradient Descent (SGD). Our study also indicates that deep learning is very promising for image forensics, such as the detection of image forgery. Conclusion: Deep learning is very promising in image forensics that is hardly discernable to human perceptions, but the performance varies over different learning models and frameworks. In addition to the models, the optimizer has a considerable impact on the final detection performance. We would recommend EfficientNet, LFnet and SRnet for seam-carving detection.

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Robust forgery detection for compressed images using CNN supervision

Cited by 40 publications

References 18 publications

Design of Automated Deep Learning-Based Fusion Model for Copy-Move Image Forgery Detection

Design of Automated Deep Learning-Based Fusion Model for Copy-Move Image Forgery Detection

Exposing Manipulated Photos and Videos in Digital Forensics Analysis

A comparison study to detect seam carving forgery in JPEG images with deep learning models

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