- Open Access
Optimal chroma-like channel design for passive color image splicing detection
© Zhao et al; licensee Springer. 2012
Received: 25 February 2012
Accepted: 30 October 2012
Published: 19 November 2012
Image splicing is one of the most common image forgeries in our daily life and due to the powerful image manipulation tools, image splicing is becoming easier and easier. Several methods have been proposed for image splicing detection and all of them worked on certain existing color channels. However, the splicing artifacts vary in different color channels and the selection of color model is important for image splicing detection. In this article, instead of finding an existing color model, we propose a color channel design method to find the most discriminative channel which is referred to as optimal chroma-like channel for a given feature extraction method. Experimental results show that both spatial and frequency features extracted from the designed channel achieve higher detection rate than those extracted from traditional color channels.
Modern information technology brings great convenience in our daily life; however, as every coin has two sides, “Seeing is not believing”  and tampered images have flooded everywhere due to the fast development of various powerful and sophisticated image manipulation and modification tools. As a result, people have gradually lost trust in digital images. Digital image forensics techniques have emerged over the past a few years to regain some trust in digital images and they can roughly be divided into two categories: the active methods [2–4] and the passive methods [5–25]. For the active approaches, certain “tags” reflecting to the image content such as digital watermark, digital fingerprint, etc., are embedded in the image. At the detection side, such “tags” are extracted and analyzed to authenticate the integrity of the image. However, in real applications on the Internet, it is impossible to restrict all the images to be watermarked before uploading, which limits the application of the active approaches. On the other hand, the passive approaches aim to authenticate the digital image based on certain underlying characteristics of the natural images and thus no prior information is required. Among all the image forgeries, image splicing can be considered as the most fundamental and common operation. It is a process that creates a composite image by cropping and pasting regions from one or more images. In this article, we concern with the passive (blind) digital image forensics methods for image splicing detection.
In recent years, many researchers have proposed various methods to detect image splicing. These methods can roughly be divided into three categories: statistical feature-based, lighting condition-based, and camera-based approaches. For statistical feature-based methods, the composite image may be quite perceptual deceiving but the tampering operation would change the underlying statistical characteristics of the original image. The statistical feature-based methods were therefore proposed to discover the composite image using such cues from these statistical features [8–11]. For example, it is observed in  that the splicing process will introduce a discontinuity to a composite signal at the spliced point and thus the lack of smoothness can be regarded as a departure from a normal signal due to a perturbation of bipolar signal. Based on the above assumptions, Ng et al.  modeled the image splicing as perturbation of the authentic counterpart with a bipolar signal. They analyzed the response of the bicoherence magnitude and phase features to detect image splicing based on the proposed model. They stated that image splicing increases the value of bicoherence magnitude and contributes to a phase bias at ±90°. Thus, the two features can be treated as discriminative features for image splicing detection. Shi et al.  proposed a natural image model for image splicing detection, the statistical feature consists of moments of characteristic functions of wavelet sub-bands and first-order Markov transition probabilities of neighboring difference block DCT array (DCT Markov). All the features extracted were combined into a vector and the vector was treated as distinguishing features for splicing detection. When composing an image, it is difficult for the forger to match the lighting conditions. Therefore, inconsistencies of light directions can be used as another evidence for revealing the tampering and lighting condition-based methods were proposed in [12–14]. Johnson and Farid [12, 13] modeled the lighting environment with a five-dimensional vector and the model approximated the lighting with a linear combination of spherical harmonics. Inconsistencies in the model across the image detected were treated as evidences of image tampering. In some environments (especially the indoor scene), the light source may give rise to a specular highlight on the human eyes. Johnson and Farid  showed that the direction to a light source could be estimated from the specular highlight and inconsistencies in lighting conditions across the image were then used to infer splicing traces of digital image. Due to the imperfection of digital camera, some artifacts (e.g., CFA interpolation, chromatic aberration, and sensor noise) are introduced in the imaging process; camera-based methods [15–23] have modeled artifacts introduced in the image processing and inconsistencies of these artifacts can be treated as evidence of splicing. CFA interpolation (demosaicing) is commonly used in the imaging process of single CCD digital cameras. For manipulated images, the demosaicing regularity will be destroyed. Based on this ground truth, Cao and Kot  proposed an ensemble manipulation detection framework (i.e., FusionBoost) to detect a range of manipulations on local image patches. This framework is composed of a set of lightweight manipulation detectors and each of these detectors is trained by CFA correlation-related features to detect some specific manipulations. All the classification results from these detectors are finally combined by the FusionBoost algorithm and thus the ensemble classifier can detect various image manipulations.
The existing splicing detection methods introduced above are usually designed for gray-scale images; however, most images on the Internet are color images and chromatic information may provide additional cues for splicing detection. Since image splicing detection can be treated as the problem of detecting weak signal (splicing) in the background of strong signal (image contents), removing the strong signal while preserving the weak signal is of vital importance for splicing detection . However, luma channel (∗Y ) which is used most frequently in the splicing detection work preserves much more image contents than the chromatic channels (e.g., Cb and Cr) . Wang et al. , therefore, investigated the effectiveness of chromatic channels in detecting image splicing. They employed gray-level co-occurrence matrix (GLCM) as discriminative features for classification and the experimental results showed that features extracted from chromatic channels achieved much better performance than those extracted from luma channel. In all the approaches mentioned above, various kinds of features were extracted from the existing color channels; however, what kind of chromatic channel is most discriminative in image splicing detection has not been well addressed yet to the best of the authors’ knowledge. Based on the work  and our previous work , Cb and Cr have proved their superiority in detecting image splicing. Since Cb and Cr which are the linear transforms of R, G, and B channels can be used for image splicing detection, other linear transforms of R, G, and B (i.e., chroma-like channels) may also be employed for image splicing detection. In this article, we propose a way of designing an optimal chroma-like channel which can best differentiate the spliced images from the natural ones. Feature extraction methods proposed in [9, 11, 24, 25] are used to test the effectiveness of the designed channel and the experimental results show that features extracted from the designed chroma-like channel can achieve higher detection rate than those extracted from the traditional color channels.
The rest of this article is organized as follows. Section 2 explains the underlying rationale of using inter-channel information in splicing detection. Details of the proposed chromatic channel design and splicing detection approaches are elaborated in Section 3. In Section 4, the framework of image splicing detection using optimal chroma-like channel is given and several existing image splicing detection methods to be employed in our experiment are introduced. Section 5 shows the experimental results and the corresponding performance analysis. Finally, conclusions are drawn in Section 6.
2 Use of inter-channel information
Based on the above analysis, it can be concluded that (1) the existing color models are application-specific; however, there is no splicing detection-oriented color model to the best of the authors’ knowledge; (2) some of the edges (or splicing artifacts) are difficult to be detected in a single channel; (3) multi-channel information should be considered in the process of image splicing detection.
3 Proposed method
Since image splicing detection can be regarded as detecting weak signal (splicing artifacts) in the background of strong signal (image contents), detecting splicing artifacts in Y channel which preserves most of the image contents is usually a difficult task. In contrary, Cb and Cr channels contain chromatic information which is less related to the image content. Therefore, detecting image splicing in chromatic channels can be regarded as detecting weak signal in the background of weak signal which helps reduce the detection difficulties . However, some splicing artifacts may be lost in any isolate chromatic channel as illustrated in Figure 2. Considering the above issues, we aim to design a channel which removes the influences of the image content while preserves the splicing artifacts as much as possible.
Therefore, deriving the optimal chroma-like channel (i.e., optimal [α∗,β∗,−α∗−β∗]) is equivalent to finding the largest hyperplane margin among candidate feature spaces (features extracted from chroma-like channels) which are mapped into higher spaces using gaussian kernel. Since there is no direct parameter optimization scheme solving (16) and (17), a numerical optimization method is employed to obtain the optimal θ defined as θ=[α,β] for marking convenience, which consists of two steps: initial parameter selection and local optimum searching.
Set initial parameter θ 0, termination condition ε and let i=0.
Compute , and update the parameter vector θ i + 1by (20) and (21), let i=i + 1.
Repeat step (2) until and the optimal parameter vector is obtained by θ ∗=θ i .
4 Splicing detection using optimal chroma-like channel
In this section, we will present the general framework of our proposed method for image splicing detection. Besides that, four commonly used image splicing detection features which will be employed to test the effectiveness of proposed method are briefly introduced.
4.1 Framework of optimal chroma-like channel design for image splicing detection
4.2 Feature extraction methods
The aim of optimal chroma-like channel is to find the most discriminative channel for a specific feature extraction method. In order to test the effectiveness of proposed method, four widely used feature extraction methods are employed in our experimental work, they are, GLCM, RLRN, DCT Markov, and 42DMoments[9, 11].
Let x(u v) be the image to be detected, directional GLCM, i.e., GLC M τ (τ=0°,45°,90°,135°), of thresholded neighboring difference edge images are used as discriminative features for color image splicing detection . Since GLC M τ is usually large and sparse, a predefined threshold is then implemented on the edge image in order to get a reasonable size of GLC M τ . Finally, GLC M0°GLC M45°GLC M90°and GLC M135° are treated as features for classification.
4.2.3 DCT Markov
where ω i and ω j are the neighboring two states, D0°(r s)and D90°(r s)are the adjacent difference block DCT arrays along horizontal and vertical directions, respectively, P0°(ω j |ω i ) (or P90°(ω j |ω i )) is the transition probabilities from state ω i to state ω j along horizontal (or vertical) direction, δ is the δ function. All the elements in P0°and P90° are treated as discriminative features for image splicing detection.
4.2.4 42D Moments
where H(x i ) is the 1D characteristic function of each wavelet sub-band, K is the total number of different values in a sub-band, l(l=1,2,3) is the order of moments, and H(u i v j ) is the 2D characteristic function of 2D histogram. For a given image, first, one-level Haar wavelet decomposition is implemented on the original image and the corresponding prediction error image. Then, characteristic functions of histograms (1D and 2D) of each sub-band are computed. Finally, first-, second-, and third-order moments of characteristic functions are computed via (25) for classification.
5 Experimental results and performance analysis
Optimal θ ∗ for feature extraction methods
(−0. 0663,0. 3906)
(0. 0845,0. 6074)
(−0. 3977,0. 5499)
(0. 400,−0. 800)
Compared with the gray-scale images, color images contain more information (inter-channel information) for image splicing detection. Recent research has shown that the color model selection is quite important for many computer vision algorithms, and features extracted from various color channels have different discriminative power. For the above reasons, the main objective of this study is to find an optimal color channel that can achieve the highest discriminative power for image splicing detection. Similar to the idea of finding the hyperplane with largest margin in the given feature space in SVM, our goal is to search for the most discriminative hyperplane (i.e., with the highest detecting accuracy) among all the candidate feature spaces (features extracted from chroma-like channels). Four widely used features for image splicing detection are employed to test the effectiveness of the proposed chroma-like channel. Experimental results have verified that all the four features extracted from the designed chroma-like channel achieve better class separability than those extracted from traditional color channels. In the future work, we are trying to reduce the computational complexity by narrowing the searching area of initial θ.
This research work was funded by the National Natural Science Foundation of China (61071152, 60702043), 973 Program (2010CB731403, 2010CB731406) of China, Shanghai Educational Development Foundation and National “Twelfth Five-Year” Plan for Science & Technology Support (2012BAH38B04).
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