In today's age of internet and social media, one can find an enormous volume of forged images on-line. These images have been used in the past to convey falsified information and achieve harmful intentions. The spread and the effect of the social media only makes this problem more severe. While creating forged images has become easier due to software advancements, there is no automated algorithm which can reliably detect forgery. Image forgery detection can be seen as a subset of image understanding problem. Human performance is still the gold-standard for these type of problems when compared to existing state-of-art automated algorithms. We conduct a subjective evaluation test with the aid of eye-tracker to investigate into human factors associated with this problem. We compare the performance of an automated algorithm and humans for forgery detection problem. We also develop an algorithm which uses the data from the evaluation test to predict the difficulty-level of an image (the difficulty-level of an image here denotes how difficult it is for humans to detect forgery in an image. Terms such as "Easy difficult image" will be used in the same context). The experimental results presented in this paper should facilitate development of better algorithms in the future.
 We present a theoretical analysis of the relationship between incoming radiance and irradiance. Specifically, we address the question of whether it is possible to compute the incident radiance from knowledge of the irradiance at all surface orientations. This is a fundamental question in computer vision and inverse radiative transfer. We show that the irradiance can be viewed as a simple convolution of the incident illumination, i.e., radiance and a clamped cosine transfer function. Estimating the radiance can then be seen as a deconvolution operation. We derive a simple closed-form formula for the irradiance in terms of spherical harmonic coefficients of the incident illumination and demonstrate that the odd-order modes of the lighting with order greater than 1 are completely annihilated. Therefore these components cannot be estimated from the irradiance, contradicting a theorem that is due to Preisendorfer. A practical realization of the radiance-from-irradiance problem is the estimation of the lighting from images of a homogeneous convex curved Lambertian surface of known geometry under distant illumination, since a Lambertian object reflects light equally in all directions proportional to the irradiance. We briefly discuss practical and physical considerations and describe a simple experimental test to verify our theoretical results. Verifying the integrity of digital images and detecting the traces of tampering without using any protecting pre-extracted or pre-embedded information have become an important and hot research field. The popularity of this field and the rapid growth in papers published during the last years have put considerable need on creating a complete bibliography addressing published papers in this area. In this paper, an extensive list of blind methods for detecting image forgery is presented. By the word blind we refer to those methods that use only the image function. An attempt has been made to make this paper complete by listing most of the existing references and by providing a detailed classification group.  Image splicing is a commonly used technique in image tampering. This paper presents a novel approach to passive detection of image splicing. In the proposed scheme, the image splicing detection problem is tackled as a twoclass classification problem under the pattern recognition framework. Considering the high non-linearity and non-stationarity nature of image splicing operation, a recently developed Hilbert-Huang transform (HHT) is utilized to generate features for classification. Furthermore, a well established statistical natural image model based on moments of characteristic functions with wavelet decomposition is employed to distinguish the spliced images from the authentic images. We use support vector machine (SVM) as the classifier. The initial experimental results demonstrate that the proposed scheme outperforms the prior arts.  As the advent and growing popularity of image editing software, digital images can be manipulated easily without leaving obvious visual clues. If the tampered images are abused, it may lead to potential social, legal or private consequences. To this end, it’s very necessary and also challenging to find effective methods to detect digital image forgeries. In this paper, a fast method to detect image copy-move forgery is proposed based on the SURF (Speed up Robust Features) descriptors, which are invariant to rotation, scaling etc. Results of experiments indicate that the proposed method is valid in detecting the image region duplication and quite robust to additive noise and blurring. Copy-move forgery is one of the most common type of tampering in digital images. Copy-moves are parts of the image that are copied and pasted onto another part of the same image. Detection methods in general use block-matching methods, which first divide the image into overlapping blocks and then extract features from each block, assuming similar blocks will yield similar features. In this paper we present a block-based approach which exploits texture as feature to be extracted from blocks. Our goal is to study if texture is well suited for the specific application, and to compare performance of several texture descriptors. Tests have been made on both uncompressed and JPEG compressed images. We describe a geometric technique to detect physically inconsistent arrangements of shadows in an image. This technique combines multiple constraints from cast and attached shadows to constrain the projected location of a point light source. The consistency of the shadows is posed as a linear programming problem. A feasible solution indicates that the collection of shadows is physically plausible, while a failure to find a solution provides evidence of photo tampering.