Splicebuster: A new blind image splicing detector

Cozzolino, Davide; Poggi, Gianfranco; Verdoliva, Luisa

doi:10.1109/wifs.2015.7368565

Cited by 203 publications

(175 citation statements)

References 21 publications

Supporting

Mentioning

171

Contrasting

Order By: Relevance

“…Here, we use the very same blind localization algorithm proposed for Splicebuster [28]. By so doing, we obtain an objective measure of the improvement granted by adopting the image noiseprint in place of the third-order image residual used in [28]. The algorithm assumes that the pristine and manipulated parts of the image are characterized by different models.…”

Section: A Forgery Localization Based On Noiseprintsmentioning

confidence: 99%

See 1 more Smart Citation

Noiseprint: A CNN-Based Camera Model Fingerprint

Cozzolino

Verdoliva

2020

IEEE Trans.Inform.Forensic Secur.

Self Cite

282

272

View full text Add to dashboard Cite

Forensic analyses of digital images rely heavily on the traces of in-camera and out-camera processes left on the acquired images. Such traces represent a sort of camera fingerprint. If one is able to recover them, by suppressing the high-level scene content and other disturbances, a number of forensic tasks can be easily accomplished. A notable example is the PRNU pattern, which can be regarded as a device fingerprint, and has received great attention in multimedia forensics. In this paper we propose a method to extract a camera model fingerprint, called noiseprint, where the scene content is largely suppressed and model-related artifacts are enhanced. This is obtained by means of a Siamese network, which is trained with pairs of image patches coming from the same (label +1) or different (label −1) cameras. Although noiseprints can be used for a large variety of forensic tasks, here we focus on image forgery localization. Experiments on several datasets widespread in the forensic community show noiseprint-based methods to provide state-of-the-art performance.

show abstract

Section: A Forgery Localization Based On Noiseprintsmentioning

confidence: 99%

“…2. From left to right: the forged image, its noiseprint, the noise residual obtained using a Wavelet-based denoising filter [29] (a tool commonly used for PRNU extraction) and the noise residual obtained through a 3rd order derivative filter (used in the Splicebuster algorithm [28]). seen as a possible clue of manipulation.…”

Section: Introductionmentioning

confidence: 99%

Noiseprint: A CNN-Based Camera Model Fingerprint

Cozzolino

Verdoliva

2020

IEEE Trans.Inform.Forensic Secur.

Self Cite

282

272

View full text Add to dashboard Cite

show abstract

“…The performance of our method is compared with the following state of the art methods: Choi et al [6], based on CFA-based artifacts and explicitly designed for the estimation of hue modification, and SpliceBuster [3], based on statistical features of rich models [2] and selected for comparison since those features potentially capture local disturbances caused by local hue modification. We do not compare with [7], [8] given their strong assumption about the availability of the reference PRNU which is unrealistic in practical scenarios.…”

Section: B Setupsmentioning

confidence: 99%

“…Choi et al therefore outputs a binary map. The other method, SpliceBuster [3], returns the negative log-likelihood that a pixel is pristine. It means, a large value indicates high probability that a pixel is forged.…”

Section: B Setupsmentioning

confidence: 99%

“…More blind way to detect local disturbances is the extraction of statistical features of rich models capturing different types of neighboring dependencies [2]. These features have been proved to be effective in manipulation detection and localization, see for instance [3], [4]. Besides demosaicing, the imperfections of camera sensors also create sort of camera fingerprint, the so-called Photo-Response Nonuniformity (PRNU) noise, which is supposed to be present in every image [5].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hue Modification Localization By Pair Matching

Phan

Vascotto

Boato

2019

2019 27th European Signal Processing Conference (EUSIPCO)

View full text Add to dashboard Cite

Hue modification is the adjustment of hue property on color images. Conducting hue modification on an image is trivial, and it can be abused to falsify opinions of viewers. Since shapes, edges or textural information remains unchanged after hue modification, this type of manipulation is relatively hard to be detected and localized. Since small patches inherit the same Color Filter Array (CFA) configuration and demosaicing, any distortion made by local hue modification can be detected by patch matching within the same image. In this paper, we propose to localize hue modification by means of a Siamese neural network specifically designed for matching two inputs. By crafting the network outputs, we are able to form a heatmap which potentially highlights malicious regions. Our proposed method deals well not only with uncompressed images but also with the presence of JPEG compression, an operation usually hindering the exploitation of CFA and demosaicing artifacts. Experimental evidences corroborate the effectiveness of the proposed method.

show abstract

Document images forgery localization using a two‐stream network

Liu

Zhu

et al. 2021

Int J of Intelligent Sys

View full text Add to dashboard Cite

Document images often contain essential and sensitive information. With image editing software, one can easily manipulate the semantic meaning of the document image by copy-move, splicing, and removal, which causes many security issues. Hence, the document image forgery detection and localization are of great significance. In this paper, a novel two-stream network is proposed to detect and locate the forgery regions of document images. One stream captures forgery traces from the spatial information, including unnatural boundaries, contrast/brightness inconsistencies, and blurring. The other stream eliminates the image semantics by six residual filters, then capture the anomalous features between neighboring pixels introduced by upsampling, downsamping, and inpainting. Finally, the discriminant network is introduced to deeply fuse features from the two streams, and discriminates whether each image patch is forged or not using global average pooling and fully connected layer. Extensive experimental results on our constructed document image data set and the Security AI Challenger Program data set demonstrate the proposed two-stream network outperforms state-of-the-art methods. This proposed two-stream network is also better than each single stream, and is robust to some common forgery postprocessing operations.

show abstract

Splicebuster: A new blind image splicing detector

Cited by 203 publications

References 21 publications

Noiseprint: A CNN-Based Camera Model Fingerprint

Noiseprint: A CNN-Based Camera Model Fingerprint

Hue Modification Localization By Pair Matching

Document images forgery localization using a two‐stream network

Contact Info

Product

Resources

About