FingerprintNet: Synthesized Fingerprints for Generated Image Detection

Jeong, Yonghyun; Kim, Doyeon; Ro, Youngmin; Kim, Pyounggeon; Choi, Jongwon

doi:10.1007/978-3-031-19781-9_5

Cited by 8 publications

(3 citation statements)

References 40 publications

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“…Moreover, the backward process involves learning the denoising process, that is, from a noise‐corrupted image to a clear image. Currently, there are still very few studies carried out in the sense of using diffusion models to generate deepfake (Jeong, Kim, Ro, Kim, & Choi, 2022; Mandelli et al, 2022) and, in the same sense, few studies involving the detection of deepfake also generated by diffusion models.…”

Section: Discussion and Open Issuesmentioning

confidence: 99%

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

Passos,

Jodas,

Costa

et al. 2024

Expert Systems

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Recent advancements in deep learning generative models have raised concerns as they can create highly convincing counterfeit images and videos. This poses a threat to people's integrity and can lead to social instability. To address this issue, there is a pressing need to develop new computational models that can efficiently detect forged content and alert users to potential image and video manipulations. This paper presents a comprehensive review of recent studies for deepfake content detection using deep learning‐based approaches. We aim to broaden the state‐of‐the‐art research by systematically reviewing the different categories of fake content detection. Furthermore, we report the advantages and drawbacks of the examined works, and prescribe several future directions towards the issues and shortcomings still unsolved on deepfake detection.

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Section: Discussion and Open Issuesmentioning

confidence: 99%

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

Passos,

Jodas,

Costa

et al. 2024

Expert Systems

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show abstract

“…In the 1-and 2-class settings, our FreqNet also achieves gains of 2.9% and 0.4% compared to Ojha. Furthermore, compared to FingerprintNet (Jeong et al 2022b) tested on six unseen models, our FreqNet achieves a marked improvement in mean accuracy, rising from 82.6% to 90.6% and exhibiting a significant gain of 8.0%. Additionally, we provide results on 9 models from self-synthesis in Table 2.…”

Section: Deepfake Performance On Real-world Scenementioning

confidence: 95%

Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Domain Learning

Tan,

Zhao,

Wei

et al. 2024

AAAI

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This research addresses the challenge of developing a universal deepfake detector that can effectively identify unseen deepfake images despite limited training data. Existing frequency-based paradigms have relied on frequency-level artifacts introduced during the up-sampling in GAN pipelines to detect forgeries. However, the rapid advancements in synthesis technology have led to specific artifacts for each generation model. Consequently, these detectors have exhibited a lack of proficiency in learning the frequency domain and tend to overfit to the artifacts present in the training data, leading to suboptimal performance on unseen sources. To address this issue, we introduce a novel frequency-aware approach called FreqNet, centered around frequency domain learning, specifically designed to enhance the generalizability of deepfake detectors. Our method forces the detector to continuously focus on high-frequency information, exploiting high-frequency representation of features across spatial and channel dimensions. Additionally, we incorporate a straightforward frequency domain learning module to learn source-agnostic features. It involves convolutional layers applied to both the phase spectrum and amplitude spectrum between the Fast Fourier Transform (FFT) and Inverse Fast Fourier Transform (iFFT). Extensive experimentation involving 17 GANs demonstrates the effectiveness of our proposed method, showcasing state-of-the-art performance (+9.8\%) while requiring fewer parameters. The code is available at https://github.com/chuangchuangtan/FreqNet-DeepfakeDetection.

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“…Moreover, the backward process involves learning the denoising process, i.e., from a noise-corrupted image to a clear image. Currently, there are still very few studies carried out in the sense of using diffusion models to generate deepfake [142,143] and, in the same sense, few studies involving the detection of deepfake also generated by diffusion models.…”

Section: Opportunities and Future Challengesmentioning

confidence: 99%

A Review of Deep Learning-based Approaches for Deepfake Content Detection

Passos,

Jodas,

Costa

et al. 2023

Preprint

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Recent advancements in deep learning generative models have raised concerns as they can create highly convincing counterfeit images and videos. This poses a threat to people's integrity and can lead to social instability. To address this issue, there is a pressing need to develop new computational models that can efficiently detect forged content and alert users to potential image and video manipulations. This paper presents a comprehensive review of recent studies for deepfake content detection using deep learning-based approaches. We aim to broaden the state-of-the-art research by systematically reviewing the different categories of fake content detection. Furthermore, we report the advantages and drawbacks of the examined works and future directions towards the issues and shortcomings still unsolved on deepfake detection.

show abstract

FingerprintNet: Synthesized Fingerprints for Generated Image Detection

Cited by 8 publications

References 40 publications

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

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

Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Domain Learning

A Review of Deep Learning-based Approaches for Deepfake Content Detection

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