Detecting Image Splicing Based on Noise Level Inconsistency

Yao, Heng; Wang, Shuozhong; Zhang, Xinpeng; Qin, Chuan; Wang, Jinwei

doi:10.1007/s11042-016-3660-3

Cited by 59 publications

(28 citation statements)

References 29 publications

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“…Thus, the predicted values after their two motion compensations are approximately equal (i.e., F t−1 ∼ = F t−1 ). According to (5) and (6), the quantized value Q 2 t is greater than Q 2 t−1 . In other words, it is necessary to use a longer string of data bits to represent the t th frame than the (t − 1) th frame.…”

Section: Periodic Artifact In the P-frame String Of Data Bitsmentioning

confidence: 99%

“…At first, tampering-detection research mainly focused on images, and the existing techniques for image forgery detection can be roughly grouped into three aspects: image forensic-hashing techniques [1][2][3], image fragile-watermarking techniques [4,5], and image passive-forensic techniques [6][7][8]. Although the methods using the former two techniques can provide more accurate detection accuracy, the main disadvantage of hashing and watermarking techniques is that the hash information needs to be extracted in advance and watermark information needs to be embedded during the image-generating process.…”

Section: Introductionmentioning

confidence: 99%

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Detection of Double-Compressed H.264/AVC Video Incorporating the Features of the String of Data Bits and Skip Macroblocks

et al. 2017

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Abstract:Today's H.264/AVC coded videos have a high quality, high data-compression ratio. They also have a strong fault tolerance, better network adaptability, and have been widely applied on the Internet. With the popularity of powerful and easy-to-use video editing software, digital videos can be tampered with in various ways. Therefore, the double compression in the H.264/AVC video can be used as a first step in the study of video-tampering forensics. This paper proposes a simple, but effective, double-compression detection method that analyzes the periodic features of the string of data bits (SODBs) and the skip macroblocks (S-MBs) for all I-frames and P-frames in a double-compressed H.264/AVC video. For a given suspicious video, the SODBs and S-MBs are extracted for each frame. Both features are then incorporated to generate one enhanced feature to represent the periodic artifact of the double-compressed video. Finally, a time-domain analysis is conducted to detect the periodicity of the features. The primary Group of Pictures (GOP) size is estimated based on an exhaustive strategy. The experimental results demonstrate the efficacy of the proposed method.

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Section: Periodic Artifact In the P-frame String Of Data Bitsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of Double-Compressed H.264/AVC Video Incorporating the Features of the String of Data Bits and Skip Macroblocks

et al. 2017

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“…In the literature, a large number of algorithms have been introduced to effectively detect image splicing [1][2][3][4][5][6] and some algorithms achieved nearly perfect detection performance [2,3]. In recent years, researchers have been focusing more on image-splicing localization and achieved promising results [7] thanks to the advances in machine learning and deep learning [8][9][10]. Tampered regions of grayscale images were localized by different image-authentication techniques [11,12].…”

Section: Introductionmentioning

confidence: 99%

“…These features were then utilized as the input for an iterative clustering algorithm to estimate the tampering mask. The difference between noise levels of tampered and original regions was employed to find the splicing traces [8,13]. The noise level was estimated using principal component analysis and then clustered by a k-means algorithm to localize the spliced regions [13].…”

Section: Introductionmentioning

confidence: 99%

“…The noise level was estimated using principal component analysis and then clustered by a k-means algorithm to localize the spliced regions [13]. Nonlinear camera response function was individually used in Reference [14] or combined with noise-level function to exploit their strong relationship to localize the forged edges using a CNN [8]. Gamma transformation was used to detect splicing forgery whilst spliced region was localized based on the probabilities of overlapping blocks and pixels of the input image being gamma-transformed [15].…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Image-Retrieval Method for Image-Splicing Validation

et al. 2019

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Recently, the task of validating the authenticity of images and the localization of tampered regions has been actively studied. In this paper, we go one step further by providing solid evidence for image manipulation. If a certain image is proved to be the spliced image, we try to retrieve the original authentic images that were used to generate the spliced image. Especially for the image retrieval of spliced images, we propose a hybrid image-retrieval method exploiting Zernike moment and Scale Invariant Feature Transform (SIFT) features. Due to the symmetry and antisymmetry properties of the Zernike moment, the scaling invariant property of SIFT and their common rotation invariant property, the proposed hybrid image-retrieval method is efficient in matching regions with different manipulation operations. Our simulation shows that the proposed method significantly increases the retrieval accuracy of the spliced images.

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