Most malicious photo manipulations are created using standard image editing tools, such as Adobe Photoshop. We present a method for detecting one very popular Photoshop manipulation -- image warping applied to human faces -- using a model trained entirely using fake images that were automatically generated by scripting Photoshop itself. We show that our model outperforms humans at the task of recognizing manipulated images, can predict the specific location of edits, and in some cases can be used to "undo" a manipulation to reconstruct the original, unedited image. We demonstrate that the system can be successfully applied to real, artist-created image manipulations.
Hand-defined manipulation cues Other image forensics work has proposed to detect fake images using hand-defined cues @cite_21 . Early work detected resampling artifacts @cite_39 @cite_34 by finding periodic correlations between nearby pixels. There has also been work that detects inconsistent quantization @cite_36 , double-JPEG artifacts @cite_2 @cite_38 , and geometric inconsistencies @cite_40 . However, the operations performed by interactive image editing tools are often complex, and can be difficult to model. Our approach, by contrast, learns features appropriate for its task from a large dataset of manipulated images.
@cite_38 @cite_36 @cite_21 @cite_39 @cite_40 @cite_2 @cite_34
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Previous forensic techniques have exploited various characteristics of JPEG compression to reveal traces of manipulation in digital images. We describe a JPEG artifact that can arise depending on the choice of the mathematical operator used to convert DCT coefficients from floating-point to integer values. We show that the more commonly used floor or ceiling operators (but not the round operator) introduce a periodic artifact in the form of a single darker or brighter pixel — which we term a dimple — in 8 × 8 pixel blocks. We describe the nature of this artifact, its prevalence in commercial cameras, and how this artifact can be quantified and used to detect a wide range of digital manipulations from content-aware fill to re-sampling, airbrushing, and compositing. The unique stature of photographs as a definitive recording of events is being diminished due, in part, to the ease with which digital images can be manipulated and altered. Although good forgeries may leave no visual clues of having been tampered with, they may, nevertheless, alter the underlying statistics of an image. For example, we describe how resampling (e.g., scaling or rotating) introduces specific statistical correlations, and describe how these correlations can be automatically detected in any portion of an image. This technique works in the absence of any digital watermark or signature. We show the efficacy of this approach on uncompressed TIFF images, and JPEG and GIF images with minimal compression. We expect this technique to be among the first of many tools that will be needed to expose digital forgeries. The advent of sophisticated photo editing software has made it increasingly easier to manipulate digital images. Often visual inspection cannot definitively distinguish the resulting forgeries from authentic photographs. In response, forensic techniques have emerged to detect geometric or statistical inconsistencies that result from specific forms of photo manipulation. In this article we describe a new forensic technique that focuses on geometric inconsistencies that arise when fake reflections are inserted into a photograph or when a photograph containing reflections is manipulated. This analysis employs basic rules of reflective geometry and linear perspective projection, makes minimal assumptions about the scene geometry, and only requires the user to identify corresponding points on an object and its reflection. The analysis is also insensitive to common image editing operations such as resampling, color manipulations, and lossy compression. We demonstrate this technique with both visually plausible forgeries of our own creation and commercially produced forgeries. Abstract Due to the wide diffusion of JPEG coding standard, the image forensic community has devoted significant attention to the development of double JPEG (DJPEG) compression detectors through the years. The ability of detecting whether an image has been compressed twice provides paramount information toward image authenticity assessment. Given the trend recently gained by convolutional neural networks (CNN) in many computer vision tasks, in this paper we propose to use CNNs for aligned and non-aligned double JPEG compression detection. In particular, we explore the capability of CNNs to capture DJPEG artifacts directly from images. Results show that the proposed CNN-based detectors achieve good performance even with small size images (i.e., 64 × 64), outperforming state-of-the-art solutions, especially in the non-aligned case. Besides, good results are also achieved in the commonly-recognized challenging case in which the first quality factor is larger than the second one. This paper revisits the state-of-the-art resampling detector, which is based on periodic artifacts in the residue of a local linear predictor. Inspired by recent findings from the literature, we take a closer look at the complex detection procedure and model the detected artifacts in the spatial and frequency domain by means of the variance of the prediction residue. We give an exact formulation on how transformation parameters influence the appearance of periodic artifacts and analytically derive the expected position of characteristic resampling peaks. We present an equivalent accelerated and simplified detector, which is orders of magnitudes faster than the conventional scheme and experimentally shown to be comparably reliable.