A review of deep learning‐based approaches for deepfake content detection

Passos, Leandro A.; Jodas, Danilo; Costa, Kelton A. P.; Souza Júnior, Luis A.; Rodrigues, Douglas; Del Ser, Javier; Camacho, David; Papa, João Paulo

doi:10.1111/exsy.13570

Cited by 3 publications

(1 citation statement)

References 128 publications

(173 reference statements)

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“…Deep learning methods for identifying deepfake videos have been scrutinized regarding both their effectiveness and shortcomings [45]. Using neural networks, CNNs, and RNNs, these approaches excel in capturing intricate nuances and features suggestive of manipulation [46].…”

Section: Comparison Between Deep Learning and Ai Approaches 221 Deep-...mentioning

confidence: 99%

An Approach to Deepfake Video Detection Based on ACO-PSO Features and Deep Learning

Alhaji,

Celik,

Goel

2024

Electronics

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The rapid advancement of deepfake technology presents significant challenges in detecting highly convincing fake videos, posing risks such as misinformation, identity theft, and privacy violations. In response, this paper proposes an innovative approach to deepfake video detection by integrating features derived from ant colony optimization–particle swarm optimization (ACO-PSO) and deep learning techniques. The proposed methodology leverages ACO-PSO features and deep learning models to enhance detection accuracy and robustness. Features from ACO-PSO are extracted from the spatial and temporal characteristics of video frames, capturing subtle patterns indicative of deepfake manipulation. These features are then used to train a deep learning classifier to automatically distinguish between authentic and deepfake videos. Extensive experiments using comparative datasets demonstrate the superiority of the proposed method in terms of detection accuracy, robustness to manipulation techniques, and generalization to unseen data. The computational efficiency of the approach is also analyzed, highlighting its practical feasibility for real-time applications. The findings revealed that the proposed method achieved an accuracy of 98.91% and an F1 score of 99.12%, indicating remarkable success in deepfake detection. The integration of ACO-PSO features and deep learning enables comprehensive analysis, bolstering precision and resilience in detecting deepfake content. This approach addresses the challenges involved in facial forgery detection and contributes to safeguarding digital media integrity amid misinformation and manipulation.

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Section: Comparison Between Deep Learning and Ai Approaches 221 Deep-...mentioning

confidence: 99%

An Approach to Deepfake Video Detection Based on ACO-PSO Features and Deep Learning

Alhaji,

Celik,

Goel

2024

Electronics

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Fake video detection among secondary school students: The impact of sociocultural, media literacy and media use factors

Turós,

Kenyeres,

Szűts

2024

Telematics and Informatics Reports

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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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A review of deep learning‐based approaches for deepfake content detection

Cited by 3 publications

References 128 publications

An Approach to Deepfake Video Detection Based on ACO-PSO Features and Deep Learning

An Approach to Deepfake Video Detection Based on ACO-PSO Features and Deep Learning

Fake video detection among secondary school students: The impact of sociocultural, media literacy and media use factors

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

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