AI-Enhanced LSB Steganography Interface: Concealed Data Embedding Framework DOI

Raja Rajeswari N,

M Meenadshi

2022 8th International Conference on Smart Structures and Systems (ICSSS), Год журнала: 2023, Номер unknown, С. 1 - 4

Опубликована: Ноя. 23, 2023

Steganography involves concealing text-based secret data within non-text files such as image, audio, or video files, with the extraction of hidden taking place at its destination. This avoids detection. Therefore, it becomes challenging for anyone to detect presence a concealed message highlighting increasing importance privacy in contemporary times. The primary objective is enable clandestine communication between two individuals, typically involving processes encoding and decoding. project specifically focuses on image Steganography, allowing decoding an file. Through utilization steganography, individual can conceal substantial amount text, comprising thousands words, standard-sized image. To implement this approach, Python Image Library (PIL) employed conjunction Tkinter framework. LSB (Least Significant Bit) steganography techniques are applied encryption decryption functionalities. goal create application that employs insertion encode cover and, user-friendly manner, decode original from embedded Additionally, AI-based methodologies integrated enhance security fortify concealment information images, showcasing synergy advanced traditional steganographic methods.

Язык: Английский

High Payload Image Steganography Using DNN Classification and Adaptive Difference Expansion DOI
Shreela Dash, Dayal Kumar Behera, Subhra Swetanisha

и другие.

Wireless Personal Communications, Год журнала: 2024, Номер 134(3), С. 1349 - 1366

Опубликована: Фев. 1, 2024

Язык: Английский

Процитировано

2
An Intelligent Facial Expression Recognition System Using a Hybrid Deep Convolutional Neural Network for Multimedia Applications DOI Creative Commons
Ahmed J. Obaid,

Hassanain K. Alrammahi

Applied Sciences, Год журнала: 2023, Номер 13(21), С. 12049 - 12049

Опубликована: Ноя. 5, 2023

Recognizing facial expressions plays a crucial role in various multimedia applications, such as human–computer interactions and the functioning of autonomous vehicles. This paper introduces hybrid feature extraction network model to bolster discriminative capacity emotional features for applications. The proposed comprises convolutional neural (CNN) deep belief (DBN) series. First, spatial CNN processed static images, followed by temporal network. CNNs were fine-tuned based on expression recognition (FER) datasets. A was then applied integrate segment-level features. Deep fusion networks jointly used learn spatiotemporal discrimination purposes. Due its generalization capabilities, we multi-class support vector machine classifier classify seven basic emotions model. exhibited 98.14% performance JaFFE database, 95.29% KDEF 98.86% RaFD database. It is shown that method effective all three databases, compared with previous schemes JAFFE, KDEF, databases.

Язык: Английский

Процитировано

2
INVESTIGASI STEGO FILE MENGGUNAKAN FRAMEWORK NATIONAL INSTITUTE OF JUSTICE DOI Creative Commons

Hajar Hajar,

Hermansa Hermansa,

Ilcham Ilcham

и другие.

CONTEN Computer and Network Technology, Год журнала: 2024, Номер 4(1), С. 31 - 42

Опубликована: Авг. 1, 2024

Steganografi merupakan salah satu teknik anti-forensik yang memungkinkan pelaku kejahatan untuk menyembunyikan informasi ke dalam pesan lain, sehingga investigator akan menghadapi kesulitan mendapatkan bukti asli pada tersebut. Oleh karena itu, seorang dituntut memiliki kemampuan menemukan serta melakukan ekstraksi dengan menggunakan alat tepat saat membuka telah disisipi steganografi. Penelitian ini menganalisis digital metode static forensics menerapkan lima tahapan framework National Institute of Justice (NIJ) steganografi file disusupi berdasarkan skenario kasus melibatkan digital. Alat digunakan meliputi FTK Imager, Autopsy, WinHex, Hiderman, dan StegSpy. Hasil menunjukkan bahwa dari 10 diskenariokan steganografi, 9 berhasil diekstraksi tingkat keberhasilan 90%, sedangkan 10% lainnya tidak ditemukan Dapat disimpulkan hasil dapat dijadikan sah menurut hukum.

Процитировано

0
AI-Enhanced LSB Steganography Interface: Concealed Data Embedding Framework DOI

Raja Rajeswari N,

M Meenadshi

2022 8th International Conference on Smart Structures and Systems (ICSSS), Год журнала: 2023, Номер unknown, С. 1 - 4

Опубликована: Ноя. 23, 2023

Steganography involves concealing text-based secret data within non-text files such as image, audio, or video files, with the extraction of hidden taking place at its destination. This avoids detection. Therefore, it becomes challenging for anyone to detect presence a concealed message highlighting increasing importance privacy in contemporary times. The primary objective is enable clandestine communication between two individuals, typically involving processes encoding and decoding. project specifically focuses on image Steganography, allowing decoding an file. Through utilization steganography, individual can conceal substantial amount text, comprising thousands words, standard-sized image. To implement this approach, Python Image Library (PIL) employed conjunction Tkinter framework. LSB (Least Significant Bit) steganography techniques are applied encryption decryption functionalities. goal create application that employs insertion encode cover and, user-friendly manner, decode original from embedded Additionally, AI-based methodologies integrated enhance security fortify concealment information images, showcasing synergy advanced traditional steganographic methods.

Язык: Английский

Процитировано

1