An image tampering forensics method includes labeling an observation clue of a to-be-detected image, constructing a three-dimensional morphable model of an object of a category to which the target object belongs, estimating a three-dimensional normal vector to the supporting plane according to the observation clue, estimating a three-dimensional attitude of the target object according to the observation clue and the three-dimensional morphable model to obtain a plane normal vector to a plane where a side of target object in contact with the supporting plane is located, computing a parallelism between the target object and the supporting plane, and/or among a plurality of target objects, and judging whether the to-be-detected image is a tampered image or not according to the parallelism.

Systems and methods for insertion of a watermark into images and tampering detection of the watermarked images by a Convolutional Neural Network (CNN) technique. The traditional systems and methods provide for detecting the tampering of the watermarked images by simply identifying a presence of an inserted watermark into an image but none them provide for inserting a random sequence into input image(s) and then detect the tampering by classifying the input image(s) by a neural network. Embodiments of the present disclosure provide for insertion of the watermark into the input image(s) and tampering detection of the watermarked images by training a Convolutional Neural Network (CNN) 201 to classify the images as tampered or non-tampered, extracting random noise, obtaining non-classified watermarked images from the random noise, and obtaining, from the non-classified watermarked images, classified watermarked images and detecting an absence or a presence of the tampering based upon the classified watermarked images.

An image tampering forensics method includes labeling an observation clue of a to-be-detected image, constructing a three-dimensional morphable model of an object of a category to which the target object belongs, estimating a three-dimensional normal vector to the supporting plane according to the observation clue, estimating a three-dimensional attitude of the target object according to the observation clue and the three-dimensional morphable model to obtain a plane normal vector to a plane where a side of target object in contact with the supporting plane is located, computing a parallelism between the target object and the supporting plane, and/or among a plurality of target objects, and judging whether the to-be-detected image is a tampered image or not according to the parallelism.

An image authentication method is provided. An original image is divided into blocks. An interpolation algorithm is performed on each block so as to obtain a first image. Each pixel in the first image is mapped into an index based on a palette compression technique, so as to generate a second image. Each index is divided into multiple secret values, and a secret sharing algorithm is performed based on the secret values to obtain multiple partial shares. A transparent map is generated according to the partial shares, and a lossless image filed is generated by combining the original image with the transparent map.

A method and system of detecting image tampering, an electronic device and a storage medium. The method includes: A. carrying out block segmentation on a to-be-detected image to segment the to-be-detected image into a plurality of image small fragments, and extracting initial tampering detection features from all the image small fragments; B. encoding the extracted initial tampering detection features with a predetermined encoder to generate complicated tampering features, and determining a tampering detection result corresponding to the to-be-detected image according to the generated complicated tampering features, wherein the tampering detection result includes an image-tampered result and an image-not-tampered result. The disclosure realizes accurate detection for different types and formats of image tampering.

Forensic method for identifying forged documents. For each of a stream of incoming jpeg images, using a processor configured for determining whether jpeg image/s is a replacement forgery by determining whether a first portion of individual image which resides at a known location (known likely to be replaced by forger) within the individual jpeg image has been replaced, including: indicator, face-djpg, for the first portion at known location; computing indicator, aka nonface-djpg, for a second portion of individual image which resides at a comparison location within the jpeg image known as unlikely to be replaced by a forger; and determining whether face-djpg and nonface-djpg fulfill predetermined logical criterion and deciding whether the individual jpeg image is a replacement forgery accordingly.

A method and a device for detecting copies or near-copies of images, comprises receiving an initial image, converting the initial image to grayscale, resizing the grayed image to a reduced image having a plurality of rows and an even number of columns, computing an overall signature for the reduced image, and determining whether the initial image is a copy or near-copy of an image according to the result of a comparison between the overall signature of the reduced image and reference image signatures. The step of computing the overall signature comprises the steps of computing a row signature for each row of the reduced image, the computation being based on a comparison of values obtained statistically across subsets of symmetrical pixels in each row, and concatenating the row signatures in order to obtain an overall signature.

Methods and systems of detecting tampering in a digital image includes using hybrid large feature mining to identify one or more regions of an image in which tampering has occurred. Detecting tampering in a digital image with hybrid large feature mining may include spatial derivative large feature mining and transform-domain large feature mining. In some embodiments, known ensemble learning techniques are employed to address high feature dimensionality. detecting inpainting forgery includes mining features of a digital image under scrutiny based on a spatial derivative, mining one or more features of the digital image in a transform-domain; and detecting inpainting forgery in the digital image under scrutiny at least in part by the features mined based on the spatial derivative and at least in part by the features mined in the transform-domain.

An image forensics system estimates a camera response function (CRF) associated with a digital image, and compares the estimated CRF to a set of rules and compares the estimated CRF to a known CRF. The known CRF is associated with a make and a model of an image sensing device. The system applies a fusion analysis to results obtained from comparing the estimated CRF to a set of rules and from comparing the estimated CRF to the known CRF, and assesses the integrity of the digital image as a function of the fusion analysis.

A counterfeit identification performance attribute (SIPA) sensitivity to changes in resolution of the image for features of an image is determined. The CIPA sensitivity for the features is used to choose at least one feature to determine whether the image on a sample is a counterfeit.