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.
Rationale and Objectives. I examined whether the principles of search, detection, and decision making described for pulmonary nodule detection can be applied to lesion detection in mammographic images. Methods. The eye position of six radiologists (three staff mammographers and three radiology residents) was recorded as they searched mammograms for masses and microcalcifications. Results. True- and false-positive decisions were associated with prolonged gaze durations; false-negative decisions were associated with longer gaze durations than true-negatives. Readers with more experience tended to detect lesions earlier in the search than did readers with less experience, but those with less experience tended to spend more time overall searching the images and cover more image area than did those with more experience. Conclusion. Gaze duration is a useful predictor of missed lesions in mammography, making gaze duration a potential tool for perceptual feedback. Mammographic search for readers with different degrees of experience can be characterized by gaze durations, scan paths, and detection times. We posit that user behavior during natural viewing of images contains an abundance of information about the content of images as well as information related to user intent and user defined content importance. In this paper, we conduct experiments to better understand the relationship between images, the eye movements people make while viewing images, and how people construct natural language to describe images. We explore these relationships in the context of two commonly used computer vision datasets. We then further relate human cues with outputs of current visual recognition systems and demonstrate prototype applications for gaze-enabled detection and annotation.