The traditional two-dimensional (2D) barcode has been employed in anti-counterfeiting systems as a storage media for serial numbers. However, an attack can be initiated by simply copying the 2D barcode and attaching it to a counterfeit product. In this paper, we aim at proposing an authentication scheme with a mobile imaging device for a 2D barcode. This work presents a competitive solution among the 2D barcode authentication schemes that have been verified under mobile imaging conditions. The proposed copy-proof scheme is composed of two sets of features which are extracted by exploiting the characteristics of barcoding channel models. The proposed features identify the intrinsic differences between genuine and counterfeit barcode images in the frequency and spatial domains. An efficient two-stage barcode authentication framework is then proposed by combining the two sets of features in a cascading manner. To evaluate the practicality of the proposed authentication scheme, four databases with different devices (printers, scanners, mobile cameras), barcode sizes, and barcode designs are considered in the experiments. By comparing with the existing texture descriptors and some deep learning-based approaches, it is shown that the proposed scheme has a higher authentication accuracy under various conditions, such as cross-database, cross-size and cross-pattern experiments which study the generalities of a pre-trained model towards challenging conditions commonly found in real-world scenarios. Last but not least, the proposed scheme has been evaluated under some state-of-the-art attack scenarios where the attacker employs several realizations of genuine patterns or the deep learning-based technique to produce a counterfeit copy. The source code and data for producing the results in our experiments are available at https://bit.ly/2FOlJH7.

Recent steganalytic schemes reveal embedding traces in a promising way by using convolutional neural networks (CNNs). However, further improvements, such as exploring complementary data processing operations and using wider structures, were not extensively studied so far. In this letter, we design a new CNN in these aspects in order to better capture embedding artifacts. Specifically, on the one hand, we propose to process information diversely with a module called diverse activation module. On the other hand, we build a wide structure with parallel subnets using several filter groups for preprocessing. To accelerate the training process, we pretrain the subnets independently. Extensive experiments show that the proposed method is effective in detecting content-adaptive steganographic schemes.

Laser printer attribution is an increasing problem with several applications, such as pointing out the ownership of crime proofs and authentication of printed documents. However, as commonly proposed methods for this task are based on custom-tailored features, they are limited by modeling assumptions about printing artifacts. In this paper, we explore solutions able to learn discriminant-printing patterns directly from the available data during an investigation, without any further feature engineering, proposing the first approach based on deep learning to laser printer attribution. This allows us to avoid any prior assumption about printing artifacts that characterize each printer, thus highlighting almost invisible and difficult printer footprints generated during the printing process. The proposed approach merges, in a synergistic fashion, convolutional neural networks (CNNs) applied on multiple representations of multiple data. Multiple representations, generated through different pre-processing operations, enable the use of the small and lightweight CNNs whilst the use of multiple data enable the use of aggregation procedures to better determine the provenance of a document. Experimental results show that the proposed method is robust to noisy data and outperforms existing counterparts in the literature for this problem.

The forensic detection of median filtering has recently attracted the attention of the research community, mainly because of the median filtering potential uses for tampering and concealing image tampering traces in digital images. In this paper, we propose multi-scale and multi-perturbation soluti ons that build a highly discriminative feature space, which highlights the artifacts of median filtering by means of image quality measures. The proposed methods achieve promising results when validated with a series of real-world test cases, comprising different image compression levels, resolutions, and also a cross-dataset validation protocol.

The detection of copy-move image tampering is of paramount importance nowadays, mainly due to its potential use for misleading the opinion forming process of the general public. In this paper, we go beyond traditional forgery detectors and aim at combining different properties of copy-move detection approaches by modeling the problem on a multiscale behavior knowledge space, which encodes the output combinations of different techniques as a priori probabilities considering multiple scales of the training data. Afterward, the conditional probabilities missing entries are properly estimated through generative models applied on the existing training data. Finally, we propose different techniques that exploit the multi-directionality of the data to generate the final outcome detection map in a machine learning decision-making fashion. Experimental results on complex data sets, comparing the proposed techniques with a gamut of copy-move detection approaches and other fusion methodologies in the literature, show the effectiveness of the proposed method and its suitability for real-world applications.