3 citations · 9 across the 25 of their papers we have counts for
18 papers · 1 filter
Scaling Vision-Language Models Is Not Enough to Mitigate Bias
Ioannis Sarridis, Ioannis Kompatsiaris, Symeon Papadopoulos
Vision-Language Models (VLMs) such as CLIP are now foundational to multimodal systems, yet their robustness to spurious correlations remains poorly understood at scale. We present…
Face Age Verification Vulnerabilities Under Simple Appearance Manipulations
Ioannis Sarridis, Ioannis Kompatsiaris, Symeon Papadopoulos
Online platforms increasingly rely on automated age estimation systems to enforce minimum-age policies. Focusing on vision-based models designed for this task, concerns arise regar…
AuViRe: Audio-visual Speech Representation Reconstruction for Deepfake Temporal Localization
Christos Koutlis, Symeon Papadopoulos
With the rapid advancement of sophisticated synthetic audio-visual content, e.g., for subtle malicious manipulations, ensuring the integrity of digital media has become paramount.…
Designing and Generating Diverse, Equitable Face Image Datasets for Face Verification Tasks
Georgia Baltsou, Ioannis Sarridis, Christos Koutlis +1
Face verification is a significant component of identity authentication in various applications including online banking and secure access to personal devices. The majority of the…
"Humor, Art, or Misinformation?": A Multimodal Dataset for Intent-Aware Synthetic Image Detection
Anastasios Skoularikis, Stefanos-Iordanis Papadopoulos, Symeon Papadopoulos +1
Recent advances in multimodal AI have enabled progress in detecting synthetic and out-of-context content. However, existing efforts largely overlook the intent behind AI-generated…
VB-Mitigator: An Open-source Framework for Evaluating and Advancing Visual Bias Mitigation
Ioannis Sarridis, Christos Koutlis, Symeon Papadopoulos +1
Bias in computer vision models remains a significant challenge, often resulting in unfair, unreliable, and non-generalizable AI systems. Although research into bias mitigation has…
Composite Data Augmentations for Synthetic Image Detection Against Real-World Perturbations
Efthymia Amarantidou, Christos Koutlis, Symeon Papadopoulos +1
The advent of accessible Generative AI tools enables anyone to create and spread synthetic images on social media, often with the intention to mislead, thus posing a significant th…
Latent Reconstruction from Generated Data for Multimodal Misinformation Detection
Stefanos-Iordanis Papadopoulos, Christos Koutlis, Symeon Papadopoulos +1
Multimodal misinformation, such as miscaptioned images, where captions misrepresent an image's origin, context, or meaning, poses a growing challenge in the digital age. Due to the…
MAVias: Mitigate any Visual Bias
Ioannis Sarridis, Christos Koutlis, Symeon Papadopoulos +1
Mitigating biases in computer vision models is an essential step towards the trustworthiness of artificial intelligence models. Existing bias mitigation methods focus on a small se…
DiMoDif: Discourse Modality-information Differentiation for Audio-visual Deepfake Detection and Localization
Christos Koutlis, Symeon Papadopoulos
Deepfake technology has rapidly advanced and poses significant threats to information integrity and trust in online multimedia. While significant progress has been made in detectin…
BAdd: Bias Mitigation through Bias Addition
Ioannis Sarridis, Christos Koutlis, Symeon Papadopoulos +1
Computer vision (CV) datasets often exhibit biases that are perpetuated by deep learning models. While recent efforts aim to mitigate these biases and foster fair representations,…
Similarity over Factuality: Are we making progress on multimodal out-of-context misinformation detection?
Stefanos-Iordanis Papadopoulos, Christos Koutlis, Symeon Papadopoulos +1
Out-of-context (OOC) misinformation poses a significant challenge in multimodal fact-checking, where images are paired with texts that misrepresent their original context to suppor…
TextureCrop: Enhancing Synthetic Image Detection through Texture-based Cropping
Despina Konstantinidou, Christos Koutlis, Symeon Papadopoulos
Generative AI technologies produce increasingly realistic imagery, which, despite its potential for creative applications, can also be misused to produce misleading and harmful con…
SIDBench: A Python Framework for Reliably Assessing Synthetic Image Detection Methods
Manos Schinas, Symeon Papadopoulos
The generative AI technology offers an increasing variety of tools for generating entirely synthetic images that are increasingly indistinguishable from real ones. Unlike methods t…
SDFD: Building a Versatile Synthetic Face Image Dataset with Diverse Attributes
Georgia Baltsou, Ioannis Sarridis, Christos Koutlis +1
AI systems rely on extensive training on large datasets to address various tasks. However, image-based systems, particularly those used for demographic attribute prediction, face s…
Sum of Group Error Differences: A Critical Examination of Bias Evaluation in Biometric Verification and a Dual-Metric Measure
Alaa Elobaid, Nathan Ramoly, Lara Younes +3
Biometric Verification (BV) systems often exhibit accuracy disparities across different demographic groups, leading to biases in BV applications. Assessing and quantifying these bi…
Leveraging Representations from Intermediate Encoder-blocks for Synthetic Image Detection
Christos Koutlis, Symeon Papadopoulos
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…
FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data
Pietro Melzi, Ruben Tolosana, Ruben Vera-Rodriguez +44
Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must…