Fusion Transformer with Object Mask Guidance for Image Forgery Analysis

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00379884" target="_blank" >RIV/68407700:21230/24:00379884 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/CVPRW63382.2024.00438" target="_blank" >https://doi.org/10.1109/CVPRW63382.2024.00438</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CVPRW63382.2024.00438" target="_blank" >10.1109/CVPRW63382.2024.00438</a>

Result language

angličtina

Original language name

Fusion Transformer with Object Mask Guidance for Image Forgery Analysis

Original language description

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. Our approach can operate with an arbitrary number of forensic signals and leverages object information for their analysis -- unlike previous methods that rely on fusion schemes with few signals and often disregard image semantics. To this end we design a forensic signal stream composed of a transformer guided by an object attention mechanism associating patches that depict the same objects. In that way we incorporate object-level information from the image. Each forensic signal is processed by a different stream that adapts to its peculiarities. A token fusion transformer efficiently aggregates the outputs of an arbitrary number of network streams and generates a fused representation for each image patch. % These representations are finally processed by a long-range dependencies transformer that captures the intrinsic relations between the image patches. We assess two fusion variants on top of the proposed approach: (i) score-level fusion that fuses the outputs of multiple image forensics algorithms and (ii) feature-level fusion that fuses low-level forensic traces directly. Both variants exceed state-of-the-art performance on seven datasets for image forgery detection and localization with a relative average improvement of 12.1% and 20.4% in terms of F1. Our model is robust against traditional and novel forgery attacks and can be expanded with new signals without training from scratch. Our code is publicly available at: https://github.com/mever-team/omgfuser

Czech name

—

Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GM21-28830M" target="_blank" >GM21-28830M: Learning Universal Visual Representation with Limited Supervision</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

ISBN

979-8-3503-6547-4

ISSN

2160-7508

e-ISSN

2160-7516

Number of pages

11

Pages from-to

4345-4355

Publisher name

IEEE Computer Society

Place of publication

Los Alamitos

Event location

Seattle

Event date

Jun 16, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001327781704051