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Deepfakes represent one of the toughest challenges in the world of Cybersecurity and Digital Forensics, especially considering the high-quality results obtained with recent generative AI-based solutions.
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“An Overview on Image Forensics,”
A. Piva, · 2013
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I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, · 2014
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“Multimedia Forensics: Discovering the History of Multimedia Contents,”
S. Battiato, O. Giudice, and A. Paratore, · 2016
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“”Why Should I Trust You?” Explaining the Predictions of any Classifier,”
M. T. Ribeiro, S. Singh, and C. Guestrin, · 2016
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“Unpaired Image-To-Image Translation Using Cycle-Consistent Adversarial Networks,”
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, · 2017
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“Large Scale GAN Training for High Fidelity Natural Image Synthesis,”
A. Brock, J. Donahue, and K. Simonyan, · 2018
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“Progressive Growing of GANs for Improved Quality, Stability, and Variation,”
T. Karras, T. Aila, S. Laine, and J. Lehtinen, · 2018
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“StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation,”
Y. Choi, M. Choi, M. Kim, J.-W. Ha, S. Kim, and J. Choo, · 2018
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“Do GANs Leave Artificial Fingerprints?,”
F. Marra, D. Gragnaniello, L. Verdoliva, and G. Poggi, · 2019
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“Detecting GAN-Generated Imagery Using Saturation Cues,”
S. McCloskey and M. Albright, · 2019
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“Detecting and Simulating Artifacts in GAN Fake Images,”
X. Zhang, S. Karaman, and S. Chang, · 2019
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“GauGAN: Semantic Image Synthesis with Spatially Adaptive Normalization,”
T. Park, M.-Y. Liu, T.-C. Wang, and J.-Y. Zhu, · 2019
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“AttGAN: Facial Attribute Editing by Only Changing What You Want,”
Z. He, W. Zuo, M. Kan, S. Shan, and X. Chen, · 2019
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“Image-To-Image Translation via Group-Wise Deep Whitening-and-Coloring Transformation,”
W. Cho, S. Choi, D. K. Park, I. Shin, and J. Choo, · 2019
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“A Style-Based Generator Architecture for Generative Adversarial Networks,”
T. Karras, S. Laine, and T. Aila, · 2019
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O. Giudice, L. Guarnera, and S. Battiato, · 2021
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“Alias-Free Generative Adversarial Networks,”
T. Karras, M. Aittala, S. Laine, E. Härkönen, J. Hellsten, J. Lehtinen, and T. Aila, · 2021
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“On the Exploitation of Deepfake Model Recognition,”
L. Guarnera, O. Giudice, M. Nießner, and S. Battiato, · 2022
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“Tensor-Based Deepfake Detection In Scaled And Compressed Images,”
S. Concas, G. Perelli, G. L. Marcialis, and G. Puglisi, · 2022
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“Denoising Diffusion Probabilistic Models,”
J. Ho, A. Jain, and P. Abbeel, · 2020
Cited alongside, same era.
“Preliminary Forensics Analysis of Deepfake Images,”
L. Guarnera, O. Giudice, C. Nastasi, and S. Battiato, · 2020
Cited alongside, same era.
“CNN-Generated Images are Surprisingly Easy to Spot… for Now,”
S.-Y. Wang, O. Wang, R. Zhang, A. Owens, and A. A. Efros, · 2020
Cited alongside, same era.
“Fighting Deepfake by Exposing the Convolutional Traces on Images,”
L. Guarnera, O. Giudice, and S. Battiato, · 2020
Cited alongside, same era.
“Leveraging Frequency Analysis for Deep Fake Image Recognition,”
J. Frank, T. Eisenhofer, L. Schönherr, A. Fischer, D. Kolossa, and T. Holz, · 2020
Cited alongside, same era.
“Watch Your Up-Convolution: CNN Based Generative Deep Neural Networks Are Failing to Reproduce Spectral Distributions,”
R. Durall, M. Keuper, and J. Keuper, · 2020
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“Hierarchical text-conditional image generation with clip latents,”
A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen, · 2022
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“GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models,”
A. Q. Nichol, P. Dhariwal, A. Ramesh, P. Shyam, P. Mishkin, B. Mcgrew, I. Sutskever, and M. Chen, · 2022
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“High-Resolution Image Synthesis with Latent Diffusion Models,”
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, · 2022
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“On the Detection of Synthetic Images Generated by Diffusion Models,”
R. Corvi, D. Cozzolino, G. Zingarini, G. Poggi, K. Nagano, and L. Verdoliva, · 2023
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“Not with My Name! Inferring Artists’ Names of Input Strings Employed by Diffusion Models,”
R. Leotta, O. Giudice, L. Guarnera, and S. Battiato, · 2023
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“Unveiling the Impact of Image Transformations on Deepfake Detection: An Experimental Analysis,”
F. Cocchi, L. Baraldi, S. Poppi, M. Cornia, and R. Cucchiara, · 2023
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