DeepFake Detection Using Error Level Analysis and Deep Learning

Rafique, Rimsha; Nawaz, Marriam; Kibriya, Hareem; Masood, Momina

doi:10.1109/iccis54243.2021.9676375

Cited by 18 publications

(9 citation statements)

References 10 publications

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“…Finally passing the feature vector to "KNN" and "SVM" classifiers. The presented work achieved the perfect accuracy of 88.2% of "Shuffle-Net" via "KNN" and "Alex-Net" vector had the accuracy of 86.8% via "KNN", while the accuracy of "Shuffle Net" via "SVM" was 87.9% and "Alex Net" vector had the accuracy of 86.1% via "SVM" [11].…”

Section: Relatedworkmentioning

confidence: 79%

Detection of Deep Fake in Face Images Based Machine Learning

Sabah Abdul kareem,

Mahdi Altaei

2023

AJEST

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Fake face images is a recent critical issue of artificial intelligent due to it has directly impacts on the social lives, and may be made to imply threats against privacy, fraud, and other issues. Currently, creating fake images has become relatively simple due to the powerful yet user-friendly mobile applications that navigate in the social media world and with the invention of the Generative Adversarial Network (GAN) that provides a good quality images that might be difficult for humans to differentiate with their eyes and makes image and video manipulation simple to do, quickly spread and hard to detect, Therefor, image processing and artificial intelligence are crucial in solving such problems. That is why scientists must create technologies or algorithms to control and to avoid these various negative impacts by different detection approaches can be applied. The proposed approach is more robust than current methods when propose a model based on support vector machine as a classifiers to detect fake human faces created by machines. The first stages includes a preprocessing that start with changing images from RGB to YCbCr and then applying the gamma correction. finalize the results show that the extracted edges using Canny filter were useful for detecting fakes in face images. After that, applying two distinct methods of detection by utilizing "Support Vector Machine" with "Principal Component Analysis" and "Support Vector Machine" without "Principal Component Analysis" as a classifiers. The findings show that the highest accuracy gained is 96.8% when using the SVM with PCA while the accuracy obtained is 72.2% when using the SVM alone.

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Section: Relatedworkmentioning

confidence: 79%

Detection of Deep Fake in Face Images Based Machine Learning

Sabah Abdul kareem,

Mahdi Altaei

2023

AJEST

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“…Various machine learning algorithms, including support vector machines (SVMs), decision trees, and neural networks, have been employed to distinguish between real and deepfake content. [13] proposed an automated method for deepfake image classification, integrating deep learning and machine learning methodologies. Their framework, combining error level analysis, convolutional neural networks (CNNs), and support vector machines (SVMs), demonstrated robustness and efficiency in detecting deepfake images.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

“…Machine Learning Algorithms: Various machine learning algorithms, including support vector machines (SVMs) and decision trees, have been utilized to train models for distinguishing between real and deepfake content [13,17].…”

Section: Analysis and Interpretation Of Findingsmentioning

confidence: 99%

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Deepfake Detection and Classification of Images from Video: A Review of Features, Techniques, and Challenges

Bale,

Ochei,

Ugwu

2024

IJIIS

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The proliferation of deepfake technology poses significant challenges to the integrity and authenticity of visual content in videos, raising concerns about misinformation and deceptive practices. In this paper, we present a comprehensive review of features, techniques, and challenges related to the detection and classification of deepfake images extracted from videos. Existing literature has explored various approaches, including feature-based methods, machine learning algorithms, and deep learning techniques, to mitigate the adverse effects of deepfake content. However, challenges persist, such as the evolution of deepfake generation methods and the scarcity of diverse datasets for training detection models. To address these issues, this paper reviews related work on approaches for deepfake image detection and classification and synthesises these approaches into four categories - feature extraction, machine learning, and deep learning. The findings underscore the importance of continued research efforts in this domain to combat the harmful effects of deepfake technology on society. This study provides recommendations for future research directions, emphasizing the significance of proactive measures in mitigating the spread of manipulated visual content.

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“…The easier availability of multimedia content such as digital images and videos has enhanced the growth of tasks performed in the field of computer vision (CV). The well-known applications of CV involve object detection ( 1 ), object tracking ( 2 ), medical image analysis ( 3 – 5 ), text analysis ( 6 , 7 ), and video processing ( 8 ). The usage of CV approaches in the area of medical image analysis is assisting the practitioners to perform their jobs quickly and accurately.…”

Section: Introductionmentioning

confidence: 99%

AI-CenterNet CXR: An artificial intelligence (AI) enabled system for localization and classification of chest X-ray disease

Albahli

Nazir

2022

Front. Med.

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Machine learning techniques have lately attracted a lot of attention for their potential to execute expert-level clinical tasks, notably in the area of medical image analysis. Chest radiography is one of the most often utilized diagnostic imaging modalities in medical practice, and it necessitates timely coverage regarding the presence of probable abnormalities and disease diagnoses in the images. Computer-aided solutions for the identification of chest illness using chest radiography are being developed in medical imaging research. However, accurate localization and categorization of specific disorders in chest X-ray images is still a challenging problem due to the complex nature of radiographs, presence of different distortions, high inter-class similarities, and intra-class variations in abnormalities. In this work, we have presented an Artificial Intelligence (AI)-enabled fully automated approach using an end-to-end deep learning technique to improve the accuracy of thoracic illness diagnosis. We proposed AI-CenterNet CXR, a customized CenterNet model with an improved feature extraction network for the recognition of multi-label chest diseases. The enhanced backbone computes deep key points that improve the abnormality localization accuracy and, thus, overall disease classification performance. Moreover, the proposed architecture is lightweight and computationally efficient in comparison to the original CenterNet model. We have performed extensive experimentation to validate the effectiveness of the proposed technique using the National Institutes of Health (NIH) Chest X-ray dataset. Our method achieved an overall Area Under the Curve (AUC) of 0.888 and an average IOU of 0.801 to detect and classify the eight types of chest abnormalities. Both the qualitative and quantitative findings reveal that the suggested approach outperforms the existing methods, indicating the efficacy of our approach.

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DeepFake Detection Using Error Level Analysis and Deep Learning

Cited by 18 publications

References 10 publications

Detection of Deep Fake in Face Images Based Machine Learning

Detection of Deep Fake in Face Images Based Machine Learning

Deepfake Detection and Classification of Images from Video: A Review of Features, Techniques, and Challenges

AI-CenterNet CXR: An artificial intelligence (AI) enabled system for localization and classification of chest X-ray disease

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