Median Filtered Image Restoration and Anti-Forensics Using Adversarial Networks

Kim, Dongkyu; Jang, Han-Ul; Mun, Seung-Min; Choi, Sunghee; Lee, Heung-Kyu

doi:10.1109/lsp.2017.2782363

Cited by 67 publications

(26 citation statements)

References 17 publications

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“…• GAN-CF detection needs more investigation: In recent years with advancements in DL, several techniques have been extended to create fake image contents, e.g., such as GANs ( Goodfellow et al, 2014a;Kim et al, 2017 ). GANs have extensively been utilized to generate synthetic images visually indistinguishable from real ones.…”

Section: • Vulnerability and Fragility Of DL Methods Against Adversarmentioning

confidence: 99%

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A survey of machine learning techniques in adversarial image forensics

Nowroozi¹,

Dehghantanha²,

Parizi³

et al. 2021

Computers & Security

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Image forensic plays a crucial role in both criminal investigations (e.g., dissemination of fake images to spread racial hate or false narratives about specific ethnicity groups or political campaigns) and civil litigation (e.g., defamation). Increasingly, machine learning approaches are also utilized in image forensics. However, there are also a number of limitations and vulnerabilities associated with machine learning-based approaches (e.g., how to detect adversarial (image) examples), and there are associated real-world consequences (e.g., inadmissible evidence, or wrongful conviction). Therefore, with a focus on image forensics, this paper surveys techniques that can be used to enhance the robustness of machine learning-based binary manipulation detectors in various adversarial scenarios.

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Section: • Vulnerability and Fragility Of DL Methods Against Adversarmentioning

confidence: 99%

“…More recently, GANs have been employed as a CF attacks. For instance, Kim et al (2017) present median filtering CF attacks by considering GAN network, which can effectively erase the traces of median filter images. Bonettini et al (2020) considered Benford's law to discriminate generated images from pristine ones.…”

Section: Generative Adversarial Network (Gan)mentioning

confidence: 99%

A survey of machine learning techniques in adversarial image forensics

Nowroozi¹,

Dehghantanha²,

Parizi³

et al. 2021

Computers & Security

View full text Add to dashboard Cite

show abstract

“…The technique is different from [45] in the sense that the work in [45] hides the MF traces in a postprocessing step without using the original image information whereas in [50] both original and median filtered image information are used to hide the traces of MF in the resultant image. A recent approach based on the generative adversarial network (GAN) is proposed in [51] to deceive existing median filtering techniques.…”

Section: Median Filteringmentioning

confidence: 99%

Bibliography of digital image anti‐forensics and anti‐anti‐forensics techniques

Qureshi

El-Alfy

2019

IET Image Processing

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“…Chen et al [23] have discussed blind forensic method associated with the image forensics. Kim et al [24] have presented anti-forensic methods using deep neural networks integrated with convolution neural networks to eliminate the artifacts of filters being used. Dam et al [25] have developed a three-dimensional mechanism for reconstructing the facial structure based on the standard reflectance model for better comparison scores with a forensic sample.…”

Section: Introductionmentioning

confidence: 99%

Novel framework for optimized digital forensic for mitigating complex image attacks

Shashidhar¹,

Ramesh²

2020

IJECE

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Digital Image Forensic is significantly becoming popular owing to the increasing usage of the images as a media of information propagation. However, owing to the presence of various image editing tools and softwares, there is also an increasing threats over image content security. Reviewing the existing approaches of identify the traces or artifacts states that there is a large scope of optimization to be implmentation to further enhance teh processing. Therfore, this paper presents a novel framework that performs cost effective optmization of digital forensic tehnqiue with an idea of accurately localizing teh area of tampering as well as offers a capability to mitigate the attacks of various form. The study outcome shows that propsoed system offers better outcome in contrast to existing system to a significant scale to prove that minor novelty in design attribute could induce better improvement with respect to accuracy as well as resilience toward all potential image threats.

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Median Filtered Image Restoration and Anti-Forensics Using Adversarial Networks

Cited by 67 publications

References 17 publications

A survey of machine learning techniques in adversarial image forensics

A survey of machine learning techniques in adversarial image forensics

Bibliography of digital image anti‐forensics and anti‐anti‐forensics techniques

Novel framework for optimized digital forensic for mitigating complex image attacks

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