SOTAVerified

Image Manipulation

Image Manipulation is the process of altering or transforming an existing image to achieve a desired effect or to modify its content. This can involve various techniques and tools to enhance, modify, or create images based on specific requirements.

Papers

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Showing 126–150 of 427 papers

TitleStatusHype
Entity-Level Text-Guided Image ManipulationCode1
Content-Aware GAN CompressionCode1
MMFusion: Combining Image Forensic Filters for Visual Manipulation Detection and LocalizationCode1
Harmfully Manipulated Images Matter in Multimodal Misinformation DetectionCode1
Seamless Satellite-image SynthesisCode1
Content Authentication for Neural Imaging Pipelines: End-to-end Optimization of Photo Provenance in Complex Distribution ChannelsCode1
Exploiting Deep Generative Prior for Versatile Image Restoration and ManipulationCode1
Attribute-specific Control Units in StyleGAN for Fine-grained Image ManipulationCode1
Wavelet-Driven Generalizable Framework for Deepfake Face Forgery DetectionCode1
High-fidelity GAN Inversion with Padding SpaceCode1
High-Fidelity GAN Inversion for Image Attribute EditingCode1
FacialGAN: Style Transfer and Attribute Manipulation on Synthetic FacesCode1
Fake face detection via adaptive manipulation traces extraction networkCode1
Generalized Consistency Trajectory Models for Image ManipulationCode1
High Resolution Face Age EditingCode1
Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image GenerationCode1
Semantic Image Manipulation Using Scene GraphsCode1
Analysing Statistical methods for Automatic Detection of Image ForgeryCode1
Image Manipulation Detection by Multi-View Multi-Scale SupervisionCode1
Kornia: an Open Source Differentiable Computer Vision Library for PyTorchCode1
ForgerySleuth: Empowering Multimodal Large Language Models for Image Manipulation DetectionCode1
CycleNet: Rethinking Cycle Consistency in Text-Guided Diffusion for Image ManipulationCode1
SinFusion: Training Diffusion Models on a Single Image or VideoCode1
SRFlow: Learning the Super-Resolution Space with Normalizing FlowCode1
What Else Can Fool Deep Learning? Addressing Color Constancy Errors on Deep Neural Network PerformanceCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pix2PixHD-SIALPIPS (S1)0.44—Unverified
2TPSLPIPS (S1)0.12—Unverified