Impact of Data Preparation and CNN’s First Layer on Performance of Image Forensics: A Case Study of Detecting Colorized Images - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2019

Impact of Data Preparation and CNN’s First Layer on Performance of Image Forensics: A Case Study of Detecting Colorized Images

Résumé

In the field of image forensics, many convolutional neural network (CNN)-based forensic methods have been proposed and generally achieved the state-of-the-art performance. However, some questions are worth studying and answering regarding the trustworthiness of such methods, including for example the appropriateness of the discriminative information automatically extracted by CNN and the generalization performance on “unseen” data during the testing phase. In this paper, we study these questions in the case of a specific forensic problem of distinguishing between natural images (NIs) and colorized images (CIs). Through a series of experiments, we analyze the impact of data preparation and setting of the first layer of a recent state-of-the-art CNN-based method on the detector’s forensic performance, in particular the generalization capability. We obtain some interesting observations which can serve as useful hints for carrying out image forensics experiments. Moreover, we propose a very simple method to improve the generalization performance of colorized image detection by combining decision results from CNN models with different settings at the network’s first layer.
Fichier principal
Vignette du fichier
WIcomp19.pdf (8.28 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02321146 , version 1 (10-11-2020)

Identifiants

Citer

Weize Quan, Kai Wang, Dong-Ming Yan, Denis Pellerin, Xiaopeng Zhang. Impact of Data Preparation and CNN’s First Layer on Performance of Image Forensics: A Case Study of Detecting Colorized Images. WI '19 Companion IEEE/WIC/ACM International Conference on Web Intelligence, Oct 2019, Thessaloniki, Greece. pp.127-131, ⟨10.1145/3358695.3360890⟩. ⟨hal-02321146⟩
102 Consultations
47 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More