Results 51 to 60 of about 5,168 (229)

Universal Image Steganalytic Method [PDF]

, 2014
In the paper we introduce a new universal steganalytic method in JPEG file format that is detecting well-known and also newly developed steganographic methods.
Banoci, V., Broda, M., Bugar, G., Levicky, D.   +3 more
core   +1 more source

Deep Convolutional Neural Network to Detect J-UNIWARD

, 2017
This paper presents an empirical study on applying convolutional neural networks (CNNs) to detecting J-UNIWARD, one of the most secure JPEG steganographic method.
Ioffe Sergey, Nair Vinod, Qian Yinlong, Sedighi Vahid   +3 more
core   +1 more source

Robust steganographic framework for securing sensitive healthcare data of telemedicine using convolutional neural network

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Data is the key element that runs the modern society. Large amounts of data are being released day by day as a result of many activities. The digital data is transferred through the Internet which may be vulnerable to attacks while transmitting. Especially, the medical data is observed to be of at most importance.
Rupa Ch, Venkayya Yadlapalli, Suhana Sulthana Sk, G. Thippa Reddy, Sandeep Kautish   +4 more
wiley   +1 more source

Dwt Based Image Steganalysis

, 2014
‘Steganalysis’ is one of the challenging and attractive interests for the researchers with the development of information hiding techniques. It is the procedure to detect the hidden information from the stego created by known steganographic algorithm. In this paper, a novel feature based image steganalysis technique is proposed.
Indradip Banerjee, Souvik Bhattacharyya, Sanyal, Gautam   +2 more
openaire   +1 more source

Detecting Deepfakes Using Two‐Stream Framework Based on EfficientNet and Vision Transformer

Journal of Electrical and Computer Engineering, Volume 2026, Issue 1, 2026.
The ever‐growing threat of deepfake technology has stimulated research in deepfake forensics to identify the genuineness of media materials on the Internet. The majority of existing techniques employ the convolutional neural network (CNN) framework to identify deepfake by capturing the image’s local features.
Qiang Yang, Yijun Cai, Rong Yan, Guangbin Zhang, Gongping Yang   +4 more
wiley   +1 more source

Digital image steganography and steganalysis: A journey of the past three decades

Open Computer Science, 2020
Steganography is the science and art of covert communication. Conversely, steganalysis is the study of uncovering the steganographic process. The evolution of steganography has been paralleled by the development of steganalysis.
Sahu Aditya Kumar, Sahu Monalisa
doaj   +1 more source

Implementasi Steganografi Gambar Menggunakan Algoritma Generative Adversarial Network

SINTECH (Science and Information Technology) Journal, 2023
In the era of information technology, it is very important to protect data and information so that irresponsible parties do not misuse it. One technique for securing data is steganography. Steganography is a technique of hiding messages in a medium.
Khairunnisak Khairunnisak, Gilang Miftakhul Fahmi, Didit Suhartono   +2 more
doaj   +1 more source

Steganographer Identification

, 2019
Conventional steganalysis detects the presence of steganography within single objects. In the real-world, we may face a complex scenario that one or some of multiple users called actors are guilty of using steganography, which is typically defined as the
Breunig, Chen, Cortes, Erdogmus, Filler, Filler, Filler, Fridrich, Fridrich, Fridrich, Fridrich, Gretton, Guo, Hetzl, Holub, Holub, Holub, Holub, Ker, Ker, Ker, Ker, Ker, Ker, Ker, Ker, Kodovsky, Li, Li, Liu, Muandet, Pearson, Pevny, Pevný, Pevný, Pevný, Pevný, Rokach, Sahu, Sallee, Scholkopf, Shi, Song, Westfeld, Wu, Wu, Wu   +46 more
core   +1 more source

Enhanced image‐splicing classification: A resilient and scale‐invariant approach utilizing edge‐weighted local texture features

Journal of Forensic Sciences, Volume 70, Issue 6, Page 2297-2308, November 2025.
Abstract The spread of image editing tools demonstrates how modern mixed‐media technology enables changes in digital images. Such easy access raises severe moral and legal concerns around the potential for malicious image editing. Overcoming this difficulty will need the development of innovative approaches for the quick detection of changes in high ...
Arslan Akram, Muhammad Arfan Jaffar, Javed Rashid, Salah Mahmoud Boulaaras, Muhammad Faheem   +4 more
wiley   +1 more source

Distinguishing Computer-generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning

, 2018
Computer-generated graphics (CGs) are images generated by computer software. The~rapid development of computer graphics technologies has made it easier to generate photorealistic computer graphics, and these graphics are quite difficult to distinguish ...
Hu, Weitong, Shi, Yun-Qing, Wu, Ting, Yao, Ye, Zhang, Wei   +4 more
core   +2 more sources