Deep Learning for Privacy in Multimedia

Cavallaro, Andrea; Malekzadeh, Mohammad; Shamsabadi, Ali Shahin

doi:10.1145/3394171.3418551

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Information integrity, protection, and preservation for multimedia forensic investigation is a vital challenge [26,27]. Figure 2 presents a framework of image-based information hiding for multimedia forensics in an investigational context.…”

Section: Framework Of the Proposed Image-based Information Hidingmentioning

confidence: 99%

IMG‐forensics: Multimedia‐enabled information hiding investigation using convolutional neural network

Khan

Shaikh

Cheikhrouhou

et al. 2021

IET Image Processing

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Information hiding aims to embed a crucial amount of confidential data records into the multimedia, such as text, audio, static and dynamic image, and video. Image-based information hiding has been a significantly important topic for digital forensics. Here, active image deep steganographic approaches have come forward for hiding data. The least significant bit (LSB) steganography approach is proposed to conceal a secret message into the original image. First, the lightweight stream encryption cryptography encrypts secret information in the cover image to protect embedded information from source to destination. Whereas the encrypted embedded cover information into the carrier of stego-image with the help of the LSB and then transmit. In the proposed investigational scheme, a convolutional neural net is used. A model is trained to detect and extract patterns of image hidden features, encrypted stego-image optimization, and classify original and cover images of steganography. Through the experiment result on the forensic image database for mobile steganography of the Center for Statistics and Application in Forensic Evidence, the overall embedded and extracting that the proposed scheme can achieve information hiding as well as revealing with an accuracy rate of 95.1%. The experimental result shows the robustness of the model in terms of efficiency as compared to other state-of-the-art schemes.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Framework Of the Proposed Image-based Information Hidingmentioning

confidence: 99%

IMG‐forensics: Multimedia‐enabled information hiding investigation using convolutional neural network

Khan

Shaikh

Cheikhrouhou

et al. 2021

IET Image Processing

View full text Add to dashboard Cite

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Towards privacy aware deep learning for embedded systems

Duddu

Boutet

Shejwalkar

2022

Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing

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Role of NLP and Deep Learning for Multimedia Data Processing and Security

Wani,

Kaleem,

Abd El-Latif

2023

Recent Advancements in Multimedia Data Processing and Security

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This book chapter is a vital guide for understanding today's complex landscape of booming multimedia data and growing cybersecurity risks. It focuses on the transformative roles of natural language processing (NLP) and deep learning in both areas. The chapter starts by discussing deep learning's capabilities in multimedia data analysis before moving to its applications in cybersecurity. It then shifts to examine how NLP is revolutionizing multimedia data management through semantic understanding and context awareness. The chapter also explores the emerging area of social cybersecurity, spotlighting NLP's role in identifying and mitigating social engineering attacks and disinformation. It wraps up with key insights into future trends. Overall, this chapter serves as a comprehensive resource for applying NLP and Deep Learning techniques to multimedia data and cybersecurity challenges.

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Deep Learning for Privacy in Multimedia

Cited by 3 publications

References 17 publications

IMG‐forensics: Multimedia‐enabled information hiding investigation using convolutional neural network

IMG‐forensics: Multimedia‐enabled information hiding investigation using convolutional neural network

Towards privacy aware deep learning for embedded systems

Role of NLP and Deep Learning for Multimedia Data Processing and Security

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