3 citations · 4 across the 10 of their papers we have counts for
12 papers
Harmful Content Generation in Text-to-Image Models: Capabilities and Moderation Limitations
Paschalis Giakoumoglou, Manos Schinas, Symeon Papadopoulos
Text-to-image generative models can produce highly realistic imagery but also raise concerns about harmful misuse. While safety mechanisms exist, systematic evaluations of their ef…
Test-Time Noise Guided Adaptation for Realistic Autoregressive Video Generation
Dimitrios Karageorgiou, Symeon Papadopoulos, Ioannis Kompatsiaris +1
Autoregressive video diffusion models have enabled the generation of arbitrarily long videos by removing conditioning on future frames, thus greatly improving computational efficie…
Automated In-the-Wild Data Collection for Continual AI Generated Image Detection
Thanasis Pantsios, Dimitrios Karageorgiou, Christos Koutlis +3
The rapid advancement of generative Artificial Intelligence (AI) has introduced significant challenges for reliable AI-generated image detection. Existing detectors often suffer fr…
DiffusionPrint: Learning Generative Fingerprints for Diffusion-Based Inpainting Localization
Paschalis Giakoumoglou, Symeon Papadopoulos
Modern diffusion-based inpainting models pose significant challenges for image forgery localization (IFL), as their full regeneration pipelines reconstruct the entire image via a l…
TGIF2: Extended Text-Guided Inpainting Forgery Dataset & Benchmark
Hannes Mareen, Dimitrios Karageorgiou, Paschalis Giakoumoglou +3
Generative AI has made text-guided inpainting a powerful image editing tool, but at the same time a growing challenge for media forensics. Existing benchmarks, including our text-g…
Improving Generalization in Deepfake Detection with Face Foundation Models and Metric Learning
Stelios Mylonas, Symeon Papadopoulos
The increasing realism and accessibility of deepfakes have raised critical concerns about media authenticity and information integrity. Despite recent advances, deepfake detection…
Navigating the Challenges of AI-Generated Image Detection in the Wild: What Truly Matters?
Despina Konstantinidou, Dimitrios Karageorgiou, Christos Koutlis +3
As generative Artificial Intelligence (AI) advances, the realism of AI generated imagery has reached a threshold capable of deceiving even vigilant human observers. Yet, while curr…
Do DeepFake Attribution Models Generalize?
Spiros Baxavanakis, Manos Schinas, Symeon Papadopoulos
Recent advancements in DeepFake generation, along with the proliferation of open-source tools, have significantly lowered the barrier for creating synthetic media. This trend poses…
SAGI: Semantically Aligned and Uncertainty Guided AI Image Inpainting
Paschalis Giakoumoglou, Dimitrios Karageorgiou, Symeon Papadopoulos +1
Recent advancements in generative AI have made text-guided image inpainting - adding, removing, or altering image regions using textual prompts - widely accessible. However, genera…
Any-Resolution AI-Generated Image Detection by Spectral Learning
Dimitrios Karageorgiou, Symeon Papadopoulos, Ioannis Kompatsiaris +1
Recent works have established that AI models introduce spectral artifacts into generated images and propose approaches for learning to capture them using labeled data. However, the…
Evolution of Detection Performance throughout the Online Lifespan of Synthetic Images
Dimitrios Karageorgiou, Quentin Bammey, Valentin Porcellini +3
Synthetic images disseminated online significantly differ from those used during the training and evaluation of the state-of-the-art detectors. In this work, we analyze the perform…
TGIF: Text-Guided Inpainting Forgery Dataset
Hannes Mareen, Dimitrios Karageorgiou, Glenn Van Wallendael +2
Digital image manipulation has become increasingly accessible and realistic with the advent of generative AI technologies. Recent developments allow for text-guided inpainting, mak…