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This work focuses on AIGC detection to develop universal detectors capable of identifying various types of forgery images.
Detecting GAN generated fake images using co-occurrence matrices
Nataraj, L.; Mohammed, T. M.; Chandrasekaran, S.; Flenner, A.; Bappy, J. H.; Roy-Chowdhury, A. K.; and Manjunath, B. 2019 · 1903
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Generative Adversarial Nets
Goodfellow, I. J.; et al. 2014 · 2014
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Adam: A Method for Stochastic Optimization
Kingma, D. P.; et al. 2015 · 2015
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Deep residual learning for image Recognition
He, K.; et al. 2016 · 2016
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Photographic image synthesis with cascaded refinement networks
Chen, Q.; and Koltun, V. 2017 · 2017
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Xception: Deep learning with depthwise separable convolutions
Chollet, F. 2017 · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y.; et al. 2017 · 2017
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Large Scale GAN Training for High Fidelity Natural Image Synthesis
Brock, A.; et al. 2018 · 2018
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Learning to see in the dark
Chen, C.; Chen, Q.; Xu, J.; and Koltun, V. 2018 · 2018
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Stargan: Unified generative adversarial networks for multi-domain image-to-image translation
Choi, Y.; et al. 2018 · 2018
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Progressive Growing of GANs for Improved Quality, Stability, and Variation
Karras, T.; et al. 2018 · 2018
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In ictu oculi: Exposing ai created fake videos by detecting eye blinking
Li, Y.; et al. 2018 · 2018
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Second-order attention network for single image super-resolution
Dai, T.; Cai, J.; Zhang, Y.; Xia, S.-T.; and Zhang, L. 2019 · 2019
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A style-based generator architecture for generative adversarial networks
Karras, T.; et al. 2019 · 2019
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Diverse image synthesis from semantic layouts via conditional imle
Li, K.; Zhang, T.; and Malik, J. 2019 · 2019
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Semantic image synthesis with spatially-adaptive normalization
Park, T.; et al. 2019 · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A.; et al. 2019 · 2019
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Faceforensics++: Learning to detect manipulated facial images
Rossler, A.; et al. 2019 · 2019
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Detecting and simulating artifacts in gan fake images
Zhang, X.; et al. 2019 · 2019
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What makes fake images detectable? understanding properties that generalize
Chai, L.; et al. 2020 · 2020
Cited alongside, same era.
Leveraging frequency analysis for deep fake image Recognition
Frank, J.; et al. 2020 · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J.; et al. 2020 · 2020
Cited alongside, same era.
Global texture enhancement for fake face detection in the wild
Liu, Z.; et al. 2020 · 2020
Cited alongside, same era.
Two-branch recurrent network for isolating deepfakes in videos
Masi, I.; et al. 2020 · 2020
Cited alongside, same era.
Thinking in frequency: Face forgery detection by mining frequency-aware clues
Qian, Y.; et al. 2020 · 2020
Cited alongside, same era.
Representative forgery mining for fake face detection
Wang, C.; et al. 2021 · 2021
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Midjourney
2022 · 2022
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Wukong, 2022. 5
2022. 5 · 2022
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End-to-end reconstruction-classification learning for face forgery detection
Cao, J.; et al. 2022 · 2022
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Self-supervised learning of adversarial example: Towards good generalizations for deepfake detection
Chen, L.; et al. 2022 · 2022
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Vector quantized diffusion model for text-to-image synthesis
Gu, S.; Chen, D.; Bao, J.; Wen, F.; Zhang, B.; Chen, D.; Yuan, L.; and Guo, B. 2022 · 2022
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CNN-generated images are surprisingly easy to spot… for now
Wang, S.-Y.; et al. 2020 · 2020
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Dhariwal, P.; et al. 2021 · 2021
Cited alongside, same era.
Lips don’t lie: A generalisable and robust approach to face forgery detection
Haliassos, A.; et al. 2021 · 2021
Cited alongside, same era.
Beyond the Spectrum: Detecting Deepfakes via Re-Synthesis
He, Y.; et al. 2021 · 2021
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Hu, E. J.; Shen, Y.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; and Chen, W. 2021 · 2021
Cited alongside, same era.
Frequency-aware discriminative feature learning supervised by single-center loss for face forgery detection
Li, J.; et al. 2021 · 2021
Cited alongside, same era.
BiHPF: Bilateral High-Pass Filters for Robust Deepfake Detection
Jeong, Y.; et al. 2022 · 2022
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PEFT: State-of-the-art Parameter-Efficient Fine-Tuning methods
Mangrulkar, S.; Gugger, S.; Debut, L.; Belkada, Y.; Paul, S.; and Bossan, B. 2022 · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R.; et al. 2022 · 2022
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Detecting deepfakes with self-blended images
Shiohara, K.; et al. 2022 · 2022
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ADD: Frequency Attention and Multi-View Based Knowledge Distillation to Detect Low-Quality Compressed Deepfake Images
Woo, S.; et al. 2022 · 2022
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Towards universal fake image detectors that generalize across generative models
Ojha, U.; et al. 2023 · 2023
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Learning on Gradients: Generalized Artifacts Representation for GAN-Generated Images Detection
Tan, C.; Zhao, Y.; Wei, S.; Gu, G.; and Wei, Y. 2023 · 2023
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Generalizable synthetic image detection via language-guided contrastive learning
Wu, H.; Zhou, J.; and Zhang, S. 2023 · 2023
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Ucf: Uncovering common features for generalizable deepfake detection
Yan, Z.; Zhang, Y.; Fan, Y.; and Wu, B. 2023 · 2023
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Controlvideo: Training-free controllable text-to-video generation
Zhang, Y.; Wei, Y.; Jiang, D.; Zhang, X.; Zuo, W.; and Tian, Q. 2023 · 2023
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Forgery-aware adaptive transformer for generalizable synthetic image detection
Liu, H.; Tan, Z.; Tan, C.; Wei, Y.; Wang, J.; and Zhao, Y. 2024 · 2024
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Transcending forgery specificity with latent space augmentation for generalizable deepfake detection
Yan, Z.; Luo, Y.; Lyu, S.; Liu, Q.; and Wu, B. 2024 · 2024
Closest in time.
Genimage: A million-scale benchmark for detecting ai-generated image
Zhu, M.; Chen, H.; Yan, Q.; Huang, X.; Lin, G.; Li, W.; Tu, Z.; Hu, H.; Hu, J.; and Wang, Y. 2024 · 2024
Closest in time.