Detecting Deepfake Images Using Deep Learning Techniques and Explainable AI Methods

Abir, Wahidul Hasan; Khanam, Faria Rahman; Alam, Kazi Nabiul; Hadjouni, Myriam; Elmannai, Hela; Bourouis, Sami; Dey, Rajesh; Khan, Mohammad Monirujjaman

doi:10.32604/iasc.2023.029653

Cited by 7 publications

(2 citation statements)

References 25 publications

(37 reference statements)

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“…The significance of this research lies in its unique approach to deepfake image detection, specifically focusing on the application of three CNN architectures: VGG16, VGG19, and ResNet50 when sample size for training is small. While previous literature predominantly utilized public datasets sets in Kaggle such as DFDC dataset, FFHQ, DFFD and Flickr for training and evaluation, this study aims to fill a gap by employing a dataset that has been specifically collected for this research [1,14,15]. One notable aspect of this study is the exploration of how these CNN architectures perform when trained on a comparatively smaller dataset, in contrast to the larger public datasets commonly used in prior research.…”

Section: Methodology 21 Research Significantmentioning

confidence: 99%

Comparative Analysis of Deepfake Image Detection Method Using VGG16, VGG19 and ResNet50

Zahra Nazemi Ashani

2024

ARASET

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In the rapidly evolving digital landscape, the proliferation of online image and video sharing has reached unprecedented levels. This surge has been accompanied by the emergence of deepfake technology, powered by generative adversarial networks and deep learning techniques, enabling the creation of highly realistic fabricated images and videos. Consequently, the widespread dissemination of deepfake content on social media platforms has prompted the global community to seek effective methods for detecting and combating this issue. Recognizing that Artificial Intelligence (AI) has played a significant role in propagating deepfake challenges, researchers and experts believe that AI can also provide viable solutions. In this study, we explore the application of AI for deepfake image detection. Specifically, we focus on three convolutional neural network (CNN) algorithms—VGG16, VGG19, and ResNet—for this purpose. To evaluate the performance of these CNN models, a dataset comprising 1,200 images, including a combination of fake and real images, was utilized. The deepfake images were generated using FaceApp, a prominent tool for creating manipulated visuals. Our findings demonstrate that VGG19 outperforms both VGG16 and ResNet50, achieving an impressive accuracy rate of 98% on the test dataset when small size is small. By harnessing the power of AI, particularly through the application of CNN algorithms, this research makes a significant contribution to the field of deepfake image detection. The results underscore the efficacy of VGG19 in accurately identifying manipulated images, thereby providing valuable insights for the development of robust detection systems to combat the proliferation of deepfakes in today's digital world.

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Section: Methodology 21 Research Significantmentioning

confidence: 99%

Comparative Analysis of Deepfake Image Detection Method Using VGG16, VGG19 and ResNet50

Zahra Nazemi Ashani

2024

ARASET

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“…In the digital age, the proliferation of advanced artificial intelligence technologies has ushered in a new era of multimedia content creation, among which DeepFakes stand out for their sophistication and potential for misuse. DeepFakes, a portmanteau of "deep learning" and "fake," refer to hyper-realistic video and audio content generated by AI algorithms that can convincingly depict individuals saying or doing things they never actually did [1] . This technology, while showcasing the impressive capabilities of machine learning, harbors profound implications for society, media integrity, and security [2] [3].…”

Section: Introductionmentioning

confidence: 99%

DeepExplain: Enhancing DeepFake Detection Through Transparent and Explainable AI model

Venkateswarulu,

Srinagesh

2024

IJCAI

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The rapid advancement of digital media technologies has given rise to DeepFake videos, synthetic content generated using deep learning algorithms that can convincingly mimic real individuals' appearances and actions. This presents a significant challenge in maintaining digital content integrity, as DeepFakes pose threats to information veracity, security, and public trust. Addressing this challenge necessitates robust detection methods that not only accurately identify DeepFakes but also ensure transparency and understandability in their operations. This study introduces DeepExplain, a new approach that combine convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, augmented with explainability features to enhance the detection of DeepFakes. Utilizing the comprehensive DeepFake Detection Challenge (DFDC) dataset, DeepExplain demonstrates superior accuracy and balanced performance across essential metrics such as recall, precision, and area under the receiver operating characteristic (AUROC) curve. Crucially, by integrating explainability mechanisms like Gradientweighted Class Activation Mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP) values, DeepExplain not only identifies DeepFakes but also provides insights into the decision-making process,fostering trust and facilitating broader understanding. The findings underscore the potential of explainable and transparent AI solutions in combating the evolving threat of DeepFakes, highlighting important directions for future research and practical applications in digital media verification and security.Povetek: Študija predstavlja DeepExplain, metodo za zaznavanje DeepFake vsebin, ki združuje CNN in LSTM omrežja z mehanizmi razložljivosti. DeepExplain dosega visoko točnost na DFDC podatkovnem naboru, vključuje pa tudi Grad-CAM in SHAP za večjo transparentnost in razumevanje odločitev modela.

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Examining the Impact of Foreign Aid on Electrical Infrastructure Development in South Asian Countries: Comprehending the Correlation Between Foreign Aid and Corruption

Khan,

Saha,

Ekundayo

2024

Lecture Notes in Electrical Engineering

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Detecting Deepfake Images Using Deep Learning Techniques and Explainable AI Methods

Cited by 7 publications

References 25 publications

Comparative Analysis of Deepfake Image Detection Method Using VGG16, VGG19 and ResNet50

Comparative Analysis of Deepfake Image Detection Method Using VGG16, VGG19 and ResNet50

DeepExplain: Enhancing DeepFake Detection Through Transparent and Explainable AI model

Examining the Impact of Foreign Aid on Electrical Infrastructure Development in South Asian Countries: Comprehending the Correlation Between Foreign Aid and Corruption

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