Search Results for author: Giorgos Kordopatis-Zilos

Found 16 papers, 8 papers with code

Fusion Transformer with Object Mask Guidance for Image Forgery Analysis

no code implementations18 Mar 2024 Dimitrios Karageorgiou, Giorgos Kordopatis-Zilos, Symeon Papadopoulos

In this work, we introduce OMG-Fuser, a fusion transformer-based network designed to extract information from various forensic signals to enable robust image forgery detection and localization.

Image Forensics Image Forgery Detection

The 2023 Video Similarity Dataset and Challenge

1 code implementation15 Jun 2023 Ed Pizzi, Giorgos Kordopatis-Zilos, Hiral Patel, Gheorghe Postelnicu, Sugosh Nagavara Ravindra, Akshay Gupta, Symeon Papadopoulos, Giorgos Tolias, Matthijs Douze

The problem comprises two distinct but related tasks: determining whether a query video shares content with a reference video ("detection"), and additionally temporally localizing the shared content within each video ("localization").

Copy Detection Video Similarity

Improving Synthetically Generated Image Detection in Cross-Concept Settings

1 code implementation24 Apr 2023 Pantelis Dogoulis, Giorgos Kordopatis-Zilos, Ioannis Kompatsiaris, Symeon Papadopoulos

New advancements for the detection of synthetic images are critical for fighting disinformation, as the capabilities of generative AI models continuously evolve and can lead to hyper-realistic synthetic imagery at unprecedented scale and speed.

A Multi-Stream Fusion Network for Image Splicing Localization

no code implementations2 Dec 2022 Maria Siopi, Giorgos Kordopatis-Zilos, Polychronis Charitidis, Ioannis Kompatsiaris, Symeon Papadopoulos

In this paper, we address the problem of image splicing localization with a multi-stream network architecture that processes the raw RGB image in parallel with other handcrafted forensic signals.

InDistill: Information flow-preserving knowledge distillation for model compression

1 code implementation20 May 2022 Ioannis Sarridis, Christos Koutlis, Giorgos Kordopatis-Zilos, Ioannis Kompatsiaris, Symeon Papadopoulos

In this paper we introduce InDistill, a model compression approach that combines knowledge distillation and channel pruning in a unified framework for the transfer of the critical information flow paths from a heavyweight teacher to a lightweight student.

Knowledge Distillation Model Compression

The MeVer DeepFake Detection Service: Lessons Learnt from Developing and Deploying in the Wild

no code implementations27 Apr 2022 Spyridon Baxevanakis, Giorgos Kordopatis-Zilos, Panagiotis Galopoulos, Lazaros Apostolidis, Killian Levacher, Ipek B. Schlicht, Denis Teyssou, Ioannis Kompatsiaris, Symeon Papadopoulos

Enabled by recent improvements in generation methodologies, DeepFakes have become mainstream due to their increasingly better visual quality, the increase in easy-to-use generation tools and the rapid dissemination through social media.

DeepFake Detection Face Swapping

Leveraging Selective Prediction for Reliable Image Geolocation

no code implementations23 Nov 2021 Apostolos Panagiotopoulos, Giorgos Kordopatis-Zilos, Symeon Papadopoulos

In this paper, we define the task of image localizability, i. e. suitability of an image for geolocation, and propose a selective prediction methodology to address the task.

Fake News Detection

DnS: Distill-and-Select for Efficient and Accurate Video Indexing and Retrieval

1 code implementation24 Jun 2021 Giorgos Kordopatis-Zilos, Christos Tzelepis, Symeon Papadopoulos, Ioannis Kompatsiaris, Ioannis Patras

In this work, we propose a Knowledge Distillation framework, called Distill-and-Select (DnS), that starting from a well-performing fine-grained Teacher Network learns: a) Student Networks at different retrieval performance and computational efficiency trade-offs and b) a Selector Network that at test time rapidly directs samples to the appropriate student to maintain both high retrieval performance and high computational efficiency.

Computational Efficiency Knowledge Distillation +2

Audio-based Near-Duplicate Video Retrieval with Audio Similarity Learning

1 code implementation17 Oct 2020 Pavlos Avgoustinakis, Giorgos Kordopatis-Zilos, Symeon Papadopoulos, Andreas L. Symeonidis, Ioannis Kompatsiaris

For the robust similarity calculation between two videos, we first extract representative audio-based video descriptors by leveraging transfer learning based on a Convolutional Neural Network (CNN) trained on a large scale dataset of audio events, and then we calculate the similarity matrix derived from the pairwise similarity of these descriptors.

Retrieval Transfer Learning +1

Investigating the Impact of Pre-processing and Prediction Aggregation on the DeepFake Detection Task

no code implementations12 Jun 2020 Polychronis Charitidis, Giorgos Kordopatis-Zilos, Symeon Papadopoulos, Ioannis Kompatsiaris

In this paper, we propose a pre-processing step to improve the training data quality and examine its effect on the performance of DeepFake detection.

DeepFake Detection Face Swapping

ViSiL: Fine-grained Spatio-Temporal Video Similarity Learning

1 code implementation ICCV 2019 Giorgos Kordopatis-Zilos, Symeon Papadopoulos, Ioannis Patras, Ioannis Kompatsiaris

Subsequently, the similarity matrix between all video frames is fed to a four-layer CNN, and then summarized using Chamfer Similarity (CS) into a video-to-video similarity score -- this avoids feature aggregation before the similarity calculation between videos and captures the temporal similarity patterns between matching frame sequences.

ISVR Retrieval +2

Brenda Starr at SemEval-2019 Task 4: Hyperpartisan News Detection

no code implementations SEMEVAL 2019 Olga Papadopoulou, Giorgos Kordopatis-Zilos, Markos Zampoglou, Symeon Papadopoulos, Yiannis Kompatsiaris

In the effort to tackle the challenge of Hyperpartisan News Detection, i. e., the task of deciding whether a news article is biased towards one party, faction, cause, or person, we experimented with two systems: i) a standard supervised learning approach using superficial text and bag-of-words features from the article title and body, and ii) a deep learning system comprising a four-layer convolutional neural network and max-pooling layers after the embedding layer, feeding the consolidated features to a bi-directional recurrent neural network.

FIVR: Fine-grained Incident Video Retrieval

1 code implementation11 Sep 2018 Giorgos Kordopatis-Zilos, Symeon Papadopoulos, Ioannis Patras, Ioannis Kompatsiaris

To create the dataset, we devise a process for the collection of YouTube videos based on major news events from recent years crawled from Wikipedia and deploy a retrieval pipeline for the automatic selection of query videos based on their estimated suitability as benchmarks.

Benchmarking Retrieval +1

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