Papers Image Attribution
“Image Attribution” 태그가 달린 논문 38편 · 필터 해제
Representation and Reference Selection in Training-Free Synthetic Image Attribution
Synthetic image attribution aims at identifying the generator responsible for a given AI-generated image. Training-free reference-based attribution methods are easily scalable, since newly emerging generators can be inco…
Image AttributionOCCAM: Open-set Causal Concept explAnation and Ontology induction for black-box vision Models
Interpreting the decisions of deep image classifiers remains challenging, particularly in black-box settings where model internals are inaccessible. We introduce OCCAM, a framework for open-set causal concept explanation…
Image AttributionImageAttributionBench: How Far Are We from Generalizable Attribution?
The rapid advancement of generative AI has enabled the creation of highly realistic and diverse synthetic images, posing critical challenges for image provenance and misinformation detection. This underscores the urgent …
Image AttributionSeeing What Shouldn't Be There: Counterfactual GANs for Medical Image Attribution
Ascription of an image gives insights into the objects that influence the classification of the whole image or its pixels towards a specific category. These insights help radiologists to visualize deformities in medical …
Image AttributionCGC: Compositional Grounded Contrast for Fine-Grained Multi-Image Understanding
Although Multimodal Large Language Models (MLLMs) have advanced rapidly, they still face notable challenges in fine-grained multi-image understanding, often exhibiting spatial hallucination, attention leakage, and failur…
Image AttributionIncreFA: Breaking the Static Wall of Generative Model Attribution
As AI generative models evolve at unprecedented speed, image attribution has become a moving target. New diffusion, adversarial and autoregressive generators appear almost monthly, making existing watermark, classifier a…
Incremental LearningImage AttributionAttribution as Retrieval: Model-Agnostic AI-Generated Image Attribution
With the rapid advancement of AIGC technologies, image forensics will encounter unprecedented challenges. Traditional methods are incapable of dealing with increasingly realistic images generated by rapidly evolving imag…
Unsupervised Pre-trainingImage ClassificationDeepFake DetectionImage AttributionUnsupervised Synthetic Image Attribution: Alignment and Disentanglement
As the quality of synthetic images improves, identifying the underlying concepts of model-generated images is becoming increasingly crucial for copyright protection and ensuring model transparency. Existing methods achie…
Self-Supervised LearningImage AttributionTraining-free Source Attribution of AI-generated Images via Resynthesis
Synthetic image source attribution is a challenging task, especially in data scarcity conditions requiring few-shot or zero-shot classification capabilities. We present a new training-free one-shot attribution method bas…
Image AttributionFew-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples
AI-generated image (AIGI) attribution presents a pressing challenge that goes beyond mere AIGI detection, aiming to identify the source model or technique responsible for a synthetic image. However, most previous source …
Image AttributionImage GenerationMetaSeal: Defending Against Image Attribution Forgery Through Content-Dependent Cryptographic Watermarks
The rapid growth of digital and AI-generated images has amplified the need for secure and verifiable methods of image attribution. While digital watermarking offers more robust protection than metadata-based approaches--…
Image AttributionAEDR: Training-Free AI-Generated Image Attribution via Autoencoder Double-Reconstruction
The rapid advancement of image-generation technologies has made it possible for anyone to create photorealistic images using generative models, raising significant security concerns. To mitigate malicious use, tracing th…
Computational EfficiencyImage AttributionSampling Matters in Explanations: Towards Trustworthy Attribution Analysis Building Block in Visual Models through Maximizing Explanation Certainty
Image attribution analysis seeks to highlight the feature representations learned by visual models such that the highlighted feature maps can reflect the pixel-wise importance of inputs. Gradient integration is a buildin…
Image AttributionWILD: a new in-the-Wild Image Linkage Dataset for synthetic image attribution
Synthetic image source attribution is an open challenge, with an increasing number of image generators being released yearly. The complexity and the sheer number of available generative techniques, as well as the scarcit…
BenchmarkingImage AttributionSynthetic Image AttributionLoRAX: LoRA eXpandable Networks for Continual Synthetic Image Attribution
As generative AI image technologies become more widespread and advanced, there is a growing need for strong attribution models. These models are crucial for verifying the authenticity of images and identifying the archit…
class-incremental learningClass Incremental LearningContinual LearningDeepFake Detection+4Are handcrafted filters helpful for attributing AI-generated images?
Recently, a vast number of image generation models have been proposed, which raises concerns regarding the misuse of these artificial intelligence (AI) techniques for generating fake images. To attribute the AI-generated…
AttributeImage AttributionImage GenerationI2AM: Interpreting Image-to-Image Latent Diffusion Models via Attribution Maps
Large-scale diffusion models have made significant advancements in the field of image generation, especially through the use of cross-attention mechanisms that guide image formation based on textual descriptions. While t…
Image AttributionImage GenerationImage InpaintingBOSC: A Backdoor-based Framework for Open Set Synthetic Image Attribution
Synthetic image attribution addresses the problem of tracing back the origin of images produced by generative models. Extensive efforts have been made to explore unique representations of generative models and use them t…
AttributeImage Attributionopen-set classificationSynthetic Image AttributionHide and Seek: How Does Watermarking Impact Face Recognition?
The recent progress in generative models has revolutionized the synthesis of highly realistic images, including face images. This technological development has undoubtedly helped face recognition, such as training data a…
Data AugmentationDecoderFace RecognitionImage Attribution+1Detecting Origin Attribution for Text-to-Image Diffusion Models
Modern text-to-image (T2I) diffusion models can generate images with remarkable realism and creativity. These advancements have sparked research in fake image detection and attribution, yet prior studies have not fully e…
Fake Image DetectionImage AttributionImage Generation