Papers Synthetic Image Detection
“Synthetic Image Detection” 태그가 달린 논문 28편 · 필터 해제
VERITAS: Verification and Explanation of Realness in Images for Transparency in AI Systems
The widespread and rapid adoption of AI-generated content, created by models such as Generative Adversarial Networks (GANs) and Diffusion Models, has revolutionized the digital media landscape by allowing efficient and c…
Decision MakingSynthetic Image DetectionCO-SPY: Combining Semantic and Pixel Features to Detect Synthetic Images by AI
With the rapid advancement of generative AI, it is now possible to synthesize high-quality images in a few seconds. Despite the power of these technologies, they raise significant concerns regarding misuse. Current effor…
Synthetic Image DetectionLEGION: Learning to Ground and Explain for Synthetic Image Detection
The rapid advancements in generative technology have emerged as a double-edged sword. While offering powerful tools that enhance convenience, they also pose significant social concerns. As defenders, current synthetic im…
Artifact DetectionImage ManipulationImage Manipulation DetectionLarge Language Model+2Explainable Synthetic Image Detection through Diffusion Timestep Ensembling
Recent advances in diffusion models have enabled the creation of deceptively real images, posing significant security risks when misused. In this study, we reveal that natural and synthetic images exhibit distinct differ…
DenoisingExplanation GenerationSynthetic Image DetectionMethods and Trends in Detecting Generated Images: A Comprehensive Review
The proliferation of generative models, such as Generative Adversarial Networks (GANs), Diffusion Models, and Variational Autoencoders (VAEs), has enabled the synthesis of high-quality multimedia data. However, these adv…
BenchmarkingDeepFake DetectionFace SwappingSynthetic Image DetectionSecret Lies in Color: Enhancing AI-Generated Images Detection with Color Distribution Analysis
The advancement of Generative Adversarial Networks (GANs) and diffusion models significantly enhances the realism of synthetic images, driving progress in image processing and creative design. However, this progress …
Image RestorationMisinformationQuantizationSynthetic Image DetectionOrthogonal Subspace Decomposition for Generalizable AI-Generated Image Detection
AI-generated images (AIGIs), such as natural or face images, have become increasingly important yet challenging. In this paper, we start from a new perspective to excavate the reason behind the failure generalization in …
Face SwappingSynthetic Image DetectionPresent and Future Generalization of Synthetic Image Detectors
The continued release of increasingly realistic image generation models creates a demand for synthetic image detectors. To build effective detectors we must first understand how factors like data source diversity, traini…
BenchmarkingDiversitySynthetic Image AttributionSynthetic Image DetectionDetect Fake with Fake: Leveraging Synthetic Data-driven Representation for Synthetic Image Detection
Are general-purpose visual representations acquired solely from synthetic data useful for detecting fake images? In this work, we show the effectiveness of synthetic data-driven representations for synthetic image detect…
Synthetic Image DetectionImproving Synthetic Image Detection Towards Generalization: An Image Transformation Perspective
With recent generative models facilitating photo-realistic image synthesis, the proliferation of synthetic images has also engendered certain negative impacts on social platforms, thereby raising an urgent imperative to …
Image GenerationSynthetic Image DetectionImagiNet: A Multi-Content Benchmark for Synthetic Image Detection
Recent generative models produce images with a level of authenticity that makes them nearly indistinguishable from real photos and artwork. Potential harmful use cases of these models, necessitate the creation of robust …
Contrastive LearningMiscellaneousMisinformationSynthetic Image DetectionTextureCrop: Enhancing Synthetic Image Detection through Texture-based Cropping
Generative AI technologies produce increasingly realistic imagery, which, despite its potential for creative applications, can also be misused to produce misleading and harmful content. This renders Synthetic Image Detec…
Synthetic Image DetectionTGIF: Text-Guided Inpainting Forgery Dataset
Digital image manipulation has become increasingly accessible and realistic with the advent of generative AI technologies. Recent developments allow for text-guided inpainting, making sophisticated image edits possible w…
Image ManipulationSynthetic Image DetectionWhen Synthetic Traces Hide Real Content: Analysis of Stable Diffusion Image Laundering
In recent years, methods for producing highly realistic synthetic images have significantly advanced, allowing the creation of high-quality images from text prompts that describe the desired content. Even more impressive…
Image-to-Image TranslationSynthetic Image DetectionSIDBench: A Python Framework for Reliably Assessing Synthetic Image Detection Methods
The generative AI technology offers an increasing variety of tools for generating entirely synthetic images that are increasingly indistinguishable from real ones. Unlike methods that alter portions of an image, the crea…
BenchmarkingImage GenerationSynthetic Image DetectionE3: Ensemble of Expert Embedders for Adapting Synthetic Image Detectors to New Generators Using Limited Data
As generative AI progresses rapidly, new synthetic image generators continue to emerge at a swift pace. Traditional detection methods face two main challenges in adapting to these generators: the forensic traces of synth…
Continual LearningSynthetic Image DetectionTransfer LearningHarnessing the Power of Large Vision Language Models for Synthetic Image Detection
In recent years, the emergence of models capable of generating images from text has attracted considerable interest, offering the possibility of creating realistic images from text descriptions. Yet these advances have a…
Image CaptioningSynthetic Image DetectionBi-LORA: A Vision-Language Approach for Synthetic Image Detection
Advancements in deep image synthesis techniques, such as generative adversarial networks (GANs) and diffusion models (DMs), have ushered in an era of generating highly realistic images. While this technological progress …
Binary ClassificationImage CaptioningImage GenerationSynthetic Image DetectionLeveraging Representations from Intermediate Encoder-blocks for Synthetic Image Detection
The recently developed and publicly available synthetic image generation methods and services make it possible to create extremely realistic imagery on demand, raising great risks for the integrity and safety of online i…
Image GenerationSynthetic Image DetectionHarnessing Machine Learning for Discerning AI-Generated Synthetic Images
In the realm of digital media, the advent of AI-generated synthetic images has introduced significant challenges in distinguishing between real and fabricated visual content. These images, often indistinguishable from au…
Synthetic Image DetectionTransfer Learning