Image Splicing Detection using Camera Response Function Consistency and Automatic Segmentation

Hsu, Yu-Feng; Chang, Shih‐Fu

doi:10.1109/icme.2007.4284578

Cited by 91 publications

(47 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This may be improved by incorporating new blur-resistant feature as visual cues, such as CRF based method [7] or abnormality of local hue [8], into the framework to identify a blurred splicing boundary from surrounding "natural" boundaries. Another possible improvement is how to more effectively use edge structure information, such as fixation position or edge connectivity/curvature.…”

Section: Discussionmentioning

confidence: 99%

“…We test our proposed algorithm on the Columbia image splicing detection dataset [7] including 180 authentic(auth) and 183 spliced (splc) TIFF images of different indoor/outdoor scenes. The spliced images are created using Photoshop software with no concealment.…”

Section: Image Databasementioning

confidence: 99%

“…Both the Lipsiz regularity [4] and phase congruency approach [5] can define a normalized measure of local sharpness/smoothness from the wavelet coefficients. Some methods further dealt with post-smoothing utilizing the camera response function (CRF) [6,7], or abnormality of local hue [8]. The region based methods generally rely on a generative model of the image and utilize the inconsistent system parameters estimated from the spliced and the original regions to identify the forgery.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detect Digital Image Splicing with Visual Cues

Qiu

Huang

2009

Information Hiding

View full text Add to dashboard Cite

Abstract. Image splicing detection has been considered as one of the most challenging problems in passive image authentication. In this paper, we propose an automatic detection framework to identify a spliced image. Distinguishing from existing methods, the proposed system is based on a human visual system (HVS) model in which visual saliency and fixation are used to guide the feature extraction mechanism. An interesting and important insight of this work is that there is a high correlation between the splicing borders and the first few fixation points predicted by a visual attention model using edge sharpness as visual cues. We exploit this idea to develope a digital image splicing detection system with high performance. We present experimental results which show that the proposed system outperforms the prior arts. An additional advantage offered by the proposed system is that it provides a convenient way of localizing the splicing boundaries.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Image Databasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detect Digital Image Splicing with Visual Cues

Qiu

Huang

2009

Information Hiding

View full text Add to dashboard Cite

show abstract

“…One prominent example of these methods is the recovery of the characteristic noise pattern of camera sensors [30]. Other methods estimate the camera response function [17], demosaicing [12], or lens properties [21].…”

Section: Introductionmentioning

confidence: 99%

Scene Illumination as an Indicator of Image Manipulation

Rieß

Angelopoulou

2010

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. The goal of blind image forensics is to distinguish original and manipulated images. We propose illumination color as a new indicator for the assessment of image authenticity. Many images exhibit a combination of multiple illuminants (flash photography, mixture of indoor and outdoor lighting, etc.). In the proposed method, the user selects illuminated areas for further investigation. The illuminant colors are locally estimated, effectively decomposing the scene in a map of differently illuminated regions. Inconsistencies in such a map suggest possible image tampering. Our method is physics-based, which implies that the outcome of the estimation can be further constrained if additional knowledge on the scene is available. Experiments show that these illumination maps provide a useful and very general forensics tool for the analysis of color images.

show abstract

“…In the imaging pipeline, each individual processing component modifies the input image via a particular processing algorithm, which may leave some intrinsic fingerprint traces out the output [1] [2]. In many digital image/video-based forensic applications, the intrinsic fingerprint traces, such as the process of CFA [3][4], Camera Response Function (CRF) [5], sensor pattern noise [6][7] [8], and compression artefacts [9] can be used to detect image/video tampering such as resampling [10], copy and paste, slicing [4] [11], and double compression [12]. Besides, the imperfect information in the camera is also used for forensics application [2].…”

Section: Introductionmentioning

confidence: 99%

Video forgery detection using correlation of noise residue

Hsu

Hung

Lin

et al. 2008

2008 IEEE 10th Workshop on Multimedia Signal Processing

View full text Add to dashboard Cite

Abstract-We propose a new approach for locating forged regions in a video using correlation of noise residue. In our method, block-level correlation values of noise residual are extracted as a feature for classification. We model the distribution of correlation of temporal noise residue in a forged video as a Gaussian mixture model (GMM). We propose a twostep scheme to estimate the model parameters. Consequently, a Bayesian classifier is used to find the optimal threshold value based on the estimated parameters. Two video inpainting schemes are used to simulate two different types of forgery processes for performance evaluation. Simulation results show that our method achieves promising accuracy in video forgery detection.

show abstract

Image Splicing Detection using Camera Response Function Consistency and Automatic Segmentation

Cited by 91 publications

References 12 publications

Detect Digital Image Splicing with Visual Cues

Detect Digital Image Splicing with Visual Cues

Scene Illumination as an Indicator of Image Manipulation

Video forgery detection using correlation of noise residue

Contact Info

Product

Resources

About