MedNet: Medical deepfakes detection using an improved deep learning approach

Albahli, Saleh; Nawaz, Marriam

doi:10.1007/s11042-023-17562-5

Cited by 2 publications

(1 citation statement)

References 30 publications

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“…In addition, in order to strengthen the results of the used model in the classification process, they added an AM policy that helps the network to focus on the manipulated areas of the samples and increases the recognition potential. By performing extensive experiments on the dataset they used, they achieved an average accuracy score of 85.49% in lung CT Scan deepfake detection of their approach [11]. In this study, different versions of YOLO algorithms were applied to detect deepfake medical images.…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Effective Medical Deepfake Detection in Medical Images

Karaköse,

Yetış,

Çeçen

2024

IEEE Access

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In today's world, deepfake technology is being used to generate fake images, sounds, and videos from real images and sounds using deep learning and artificial intelligence techniques. It is possible to manipulate medical images with this technology. The manipulation of medical images can lead to incorrect diagnoses by medical professionals, disrupting the functioning of hospitals. As a result of these disruptions, hospitals may experience significant financial and life-threatening problems. In this study, it is aimed to obtain an effective deep learning-based method to detect manipulated medical images. Initially, two distinct datasets are created which contain Knee Osteoarthritis X-ray and lung CT scans. Data pre-processing and augmentation methods are applied for data standardization and variation. The instances in datasets are labeled as real or fake. The medical deepfake distinguish ability of YoloV3, YoloV5nu, YoloV5su, YoloV8n, YoloV8s, YoloV8m, YoloV8l, YoloV8x models tested on these datasets. In the analysis performed, all YOLO models showed almost full success in distinguishing Knee Osteoarthritis X-ray images. In lung CT scan images, although YoloV8 models generally achieved good performance, the YoloV5 models gave the best and worst results. While the best result was obtained from YoloV5su with a recall value of 0.997, the worst result was obtained from the YoloV5nu model with a recall value of 0.91. Furthermore, the best model (YoloV5su) works 60% faster than YoloV8x model, which has the second highest performance. The findings show that YoloV5su can be used for fast and accurate medical deepfake detection.

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Section: Introductionmentioning

confidence: 99%

A New Approach for Effective Medical Deepfake Detection in Medical Images

Karaköse,

Yetış,

Çeçen

2024

IEEE Access

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A Deep Learning Model for Detecting Fake Medical Images to Mitigate Financial Insurance Fraud

Arshed,

Mumtaz,

Gherghina

et al. 2024

Computation

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Artificial Intelligence and Deepfake Technologies have brought a new dimension to the generation of fake data, making it easier and faster than ever before—this fake data could include text, images, sounds, videos, etc. This has brought new challenges that require the faster development of tools and techniques to avoid fraudulent activities at pace and scale. Our focus in this research study is to empirically evaluate the use and effectiveness of deep learning models such as Convolutional Neural Networks (CNNs) and Patch-based Neural Networks in the context of successful identification of real and fake images. We chose the healthcare domain as a potential case study where the fake medical data generation approach could be used to make false insurance claims. For this purpose, we obtained publicly available skin cancer data and used recently introduced stable diffusion approaches—a more effective technique than prior approaches such as Generative Adversarial Network (GAN)—to generate fake skin cancer images. To the best of our knowledge, and based on the literature review, this is one of the few research studies that uses images generated using stable diffusion along with real image data. As part of the exploratory analysis, we analyzed histograms of fake and real images using individual color channels and averaged across training and testing datasets. The histogram analysis demonstrated a clear change by shifting the mean and overall distribution of both real and fake images (more prominent in blue and green) in the training data whereas, in the test data, both means were different from the training data, so it appears to be non-trivial to set a threshold which could give better predictive capability. We also conducted a user study to observe where the naked eye could identify any patterns for classifying real and fake images, and the accuracy of the test data was observed to be 68%. The adoption of deep learning predictive approaches (i.e., patch-based and CNN-based) has demonstrated similar accuracy (~100%) in training and validation subsets of the data, and the same was observed for the test subset with and without StratifiedKFold (k = 3). Our analysis has demonstrated that state-of-the-art exploratory and deep-learning approaches are effective enough to detect images generated from stable diffusion vs. real images.

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MedNet: Medical deepfakes detection using an improved deep learning approach

Cited by 2 publications

References 30 publications

A New Approach for Effective Medical Deepfake Detection in Medical Images

A New Approach for Effective Medical Deepfake Detection in Medical Images

A Deep Learning Model for Detecting Fake Medical Images to Mitigate Financial Insurance Fraud

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