A device may receive digital content and may process the digital content, with at least one of an optimization-based poisoning model or a statistical-based poisoning model, to generate at least one of first poisoning data or second poisoning data, respectively. The device may generate new digital content based on the digital content and the at least one of the first poisoning data or the second poisoning data. The device may provide the new digital content to one or more devices to be accessed by at least one deepfake model used to create fake digital content and may perform one or more actions based on the new digital content.

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 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.

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 content analyzer determines whether various types of modification have been made to images. The content analyzer computes JPEG ghosts from the images that are concatenated with the image channels to generate a feature vector. The feature vector is provided as input to a neural network that determines whether the types of modification have been made to the image. The neural network may include a constrained convolution layer and several unconstrained convolution layers. An image fake model may also be applied to determine whether the image was generated using a computer model or algorithm.

Methods and systems for identifying altered content are described herein. The system generates a fingerprint for an unverified content item and locates a plurality of content items that match the fingerprint. The system then compares corresponding frames between the unverified content item and each content item of the plurality of content items. The system identifies, based on the comparing, an altered frame in the unverified content item that does not match a corresponding frame in two or more of the plurality of content items. The system also determines that one or more frames of the unverified content item that follow the altered frame match corresponding frames in the two or more of the plurality of content items. The system then generates for display an indication that the unverified content item contains one or more altered frames.

The present invention relates to a forgery detection system and its method for biomedical experiment images, especially for molecular-biological experiment images, such as western blot (WB) and polymerase chain reaction (PCR) results. The forgery detection system mainly comprises a processing module, an image difference computing module, a thresholding module, and an image mixing module are formed in an image analyzing device in the form of a library, a variable or an operand. Moreover, the processing module has a quantization parameter establishing unit, a similar computing unit, and a pseudo background generating unit. The purpose of the image analyzing device is to display an artificial image on the input image.

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.

Disclosed is a hidden data removal method based on an autoregressive generative model which is performed by a computer device. The hidden data removal method includes receiving a source image, randomly selecting a target pixel from the source image, and inputting the source image and an identifier of the target pixel to an autoregressive generative model and restoring the target pixel from the source image. The source image is an image in which steganography-based data is hidden, and the autoregressive generative model restores the target pixel on the basis of a pixel value distribution for pixels adjacent to the target pixel in the source image.

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.