A Non-Algorithmic Forensic Approach for Hiding Data in Image Files

Sabir, Maha Farouk S.; Jones, James H.; Liu, Huan; Mbaziira, Alex V.

doi:10.1145/3193077.3193087

Cited by 5 publications

(5 citation statements)

References 10 publications

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“…DOI) related to the developed research. Examples of data sets with unique ID are GovDocs 3 (Sabir et al, 2018), a publicly available data set of government documents with 986,278 files, and Enron 4 (Iqbal et al, 2008), an e-mail data set that contains 200,399 real-life e-mails from 158 employees of the Enron corporation.…”

Section: Dissemination Conceptmentioning

confidence: 99%

Towards a conceptual model for promoting digital forensics experiments

OliveiraJr

Zorzo

Neu

2020

Forensic Science International: Digital Investigation

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Section: Dissemination Conceptmentioning

confidence: 99%

Towards a conceptual model for promoting digital forensics experiments

OliveiraJr

Zorzo

Neu

2020

Forensic Science International: Digital Investigation

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“…1. Data hiding: Data hiding techniques are designed to place digital content beyond the discovery of an individual (unlike obfuscation where content may be present but nonintelligible) (Sabir et al, 2018). Data hiding may arguably be the weakest of the antiforensic techniques available to a potential offender given the forensic capabilities available to many DF practitioners.…”

Section: Category 1: Dedicated Af Toolsmentioning

confidence: 99%

When finding nothing may be evidence of something: Anti-forensics and digital tool marks

Horsman

Errickson

2019

Science & Justice

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There are an abundance of measures available to the standard digital device users which provide the opportunity to act in an anti-forensic manner and conceal any potential digital evidence denoting a criminal act. Whilst there is a lack of empirical evidence which evaluates the scale of this threat to digital forensic investigations leaving the true extent of engagement with such tools unknown, arguably the field should take proactive steps to examine and record the capabilities of these measures. Whilst forensic science has long accepted the concept of toolmark analysis as part of criminal investigations, 'digital tool marks' (DTMs) are a notion rarely acknowledged and considered in digital investigations. DTMs are the traces left behind by a tool or process on a suspect system which can help to determine what malicious behaviour has occurred on a device. This article discusses and champions the need for DTM research in digital forensics highlighting the benefits of doing so.

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“…In this way, the ROIs of the blurred image can be recovered without distortion. A black box approach was used to select the candidate from APP0-APPn to hide the secret information in [11], so that the execution efficiency of the algorithm could be improved without destroying the structure of the image.…”

Section: A Hiding Data Using Appn Markersmentioning

confidence: 99%

“…As far as we know, the document [19] published by NIST pointed out that when AES was used as the scrambling function of the unbalanced Feistel network, it could provide 128-bit security for the FF1 model after 10 rounds of transformation; also, it could provide 128-bit security for the FF3 model after 8 rounds of transformation. However, the authors of [28] pointed out that the FF3 model used in [19] could not provide 128-bit security for a small character set (less than 2 17 ) when the round number is 8; also, the FF1 model could not achieve 128-bit security when the round number is 10, if the size of the character set is less than 2 11 . Additionally, Durak et al pointed out the same results mentioned above in [29].…”

Section: B Feistel Network and Its Round Number For Securitymentioning

confidence: 99%

See 1 more Smart Citation

Data Hiding for Ensuring the Quality of the Host Image and the Security of the Message

2019

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Concealing a message in the APPn markers of a JPEG image not only protects the security of the message but also induces no change to the quality of the image. However, from the existing literature, whether it is a plaintext message or a ciphertext message hidden in APPn, it is easy for an attacker to identify the confidentiality of the hidden message, which is not conducive to the security of the message. Inspired by the natural language processing (NLP) and format-preserving encryption (FPE), this paper proposes a data hiding method, which is focused on the quality assurance of the host image and the concealment of the plain text having complete semantics based on the NLP and FPE. This method first uses the NLP and FPE to identify and encrypt the sensitive words in the plain text and then hides the ciphertext text with the plaintext style in the APPn of a JPEG image after replacing the plaintext words with the ciphertext words that have the plaintext style. The experimental results confirm that the structure, size, and quality of the host image do not show any changes before or after the data hiding and the recovered host image is also identical to its original appearance. In addition, the strategy that the semantic similarity is regulated autonomously by the user also makes it possible to obtain the ideal ciphertext words with very low similarity. Moreover, more than 80% of the ciphertext texts have reasonable semantics. Compared with the existing literature, our algorithm has a better performance.INDEX TERMS APPn, common text, format-preserving encryption, natural language processing, quality of host image.

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A Non-Algorithmic Forensic Approach for Hiding Data in Image Files

Cited by 5 publications

References 10 publications

Towards a conceptual model for promoting digital forensics experiments

Towards a conceptual model for promoting digital forensics experiments

When finding nothing may be evidence of something: Anti-forensics and digital tool marks

Data Hiding for Ensuring the Quality of the Host Image and the Security of the Message

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