Large-Scale and Fine-Grained Evaluation of Popular JPEG Forgery Localization Schemes

11/30/2018
by   Pawel Korus, et al.
0

Over the years, researchers have proposed various approaches to JPEG forgery detection and localization. In most cases, experimental evaluation was limited to JPEG quality levels that are multiples of 5 or 10. Each study used a different dataset, making it difficult to directly compare the reported results. The goal of this work is to perform a unified, large-scale and fine-grained evaluation of the most popular state-of-the-art detectors. The obtained results allow to compare the detectors with respect to various criteria, and shed more light on the compression configurations where reliable tampering localization can be expected.

READ FULL TEXT

page 7

page 8

page 10

research
03/24/2020

A Systematic Evaluation: Fine-Grained CNN vs. Traditional CNN Classifiers

To make the best use of the underlying minute and subtle differences, fi...
research
07/20/2023

RetouchingFFHQ: A Large-scale Dataset for Fine-grained Face Retouching Detection

The widespread use of face retouching filters on short-video platforms h...
research
02/26/2023

Learning Pairwise Interaction for Generalizable DeepFake Detection

A fast-paced development of DeepFake generation techniques challenge the...
research
06/30/2015

A Large-Scale Car Dataset for Fine-Grained Categorization and Verification

Updated on 24/09/2015: This update provides preliminary experiment resul...
research
12/30/2022

A Fine-Grained Vehicle Detection (FGVD) Dataset for Unconstrained Roads

The previous fine-grained datasets mainly focus on classification and ar...
research
12/09/2019

ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition

In general, intrinsic image decomposition algorithms interpret shading a...
research
06/27/2022

PARTICUL: Part Identification with Confidence measure using Unsupervised Learning

In this paper, we present PARTICUL, a novel algorithm for unsupervised l...

Please sign up or login with your details

Forgot password? Click here to reset