We present a new method for detecting montages and, in general, recognizing images or parts of images. Image recognition is becoming increasingly important, for example, in detecting copyright infringement, disinformation that puts images in a different context, detecting child pornography in image collections. Numerous methods based on robust hashing and feature extraction, more recently also supported by machine learning, are already known for this purpose. Inverse image search solutions for users are also available here. In general, however, these methods are either only robust to a limited extent against changes such as rotation and cropping or they require a high data and computational effort. Especially when several images are copied into one another and montages are created, automated recognition has been difficult to achieve up to now.

To counter the ever increasing flood of image forgeries in the form of spliced images in social media and the web in general, we propose the novel image splicing localization CNN Nois-eSeg. NoiseSeg fuses statistical and CNN-based splicing localization methods in separate branches to leverage the benefits of both. Unique splicing anomalies that can be identified by its coarse noise separation branch, fine-grained noise feature branch and error level analysis branch all get combined in a segmentation fusion head to predict a precise localization of the spliced regions. Experiments on the DSO-1, CASIAv2, DEFACTO, IMD2020 and WildWeb image splicing datasets show that NoiseSeg outperforms most other state-of-the-art methods significantly and even up to a margin of 46.8%.

In this paper, we present a development for recognizing objects from looted excavations. Experts with required expertise are not always available where an archaeological object needs to be assessed for import, export or trade. For this purpose, we developed a smartphone app that can provide on-site assistance in the initial assessment of archaeological objects. The app sends captured images to a server for recognition and receives results with similar objects and their metadata along with an associated probability. A user can thus use these information to infer the provenance of the photographed object. To this end, a deep learning based solution was developed to identify archaeological objects, including a classifier trained using transfer learning and an image matching scheme based on deep convolutional neural networks (CNN) features. The developed application will be tested by law enforcement agencies with a total of 15 smartphones for six months starting in early October.

Identifying cultural assets is a challenging task which requires specific expertise. In this paper, a deep learning based solution to identify archaeological objects is proposed. Several additions to the ResNet CNN architecture are introduced which consolidate features from different intermediate layers by applying global pooling operations. Unlike general object recognition, identifying archaeological objects poses new challenges. To meet the special requirements in classifying antiques, a hybrid network architecture is used to learn the characteristics of objects using transfer learning, which includes a classification network and a regression network. With the help of the regression network, the age of objects can be predicted, which improves the overall performance in comparison to manually classifying the age of objects. The proposed scheme is evaluated using a public database of cultural assets and the experimental results demonstrate its significant performance in identifying antique objects.

This work discusses document security, use of OCR, and integrity verification related to printed documents. Since the underlying applications are usually documents containing sensitive personal data, a solution that does not require the entire data to be stored in a database is the most compatible. In order to allow verification to be performed by anyone, it is necessary that all the data required for this is contained on the document itself. The approach must be able to cope with different layouts so that the layout does not have to be adapted for each document. In the following, we present a concept and its implementation that allows every smartphone user to verify the authenticity and integrity of a document.