Detection of Information Hiding at Anti-Copying 2D Barcodes

• URL: http://arxiv.org/abs/2003.09316v1
• Date: Fri, 20 Mar 2020 15:06:50 GMT
• Title: Detection of Information Hiding at Anti-Copying 2D Barcodes
• Abstract summary: We propose two hidden information detection schemes at the existing anti-copying 2D barcodes. The first scheme is to use the pixel distance to detect the use of an information hiding scheme in a 2D barcode. The second scheme is first to calculate the variance of the raw signal and the covariance between the recovered signal and the raw signal.
• Score: 14.722906579780755
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper concerns the problem of detecting the use of information hiding at anti-copying 2D barcodes. Prior hidden information detection schemes are either heuristicbased or Machine Learning (ML) based. The key limitation of prior heuristics-based schemes is that they do not answer the fundamental question of why the information hidden at a 2D barcode can be detected. The key limitation of prior MLbased information schemes is that they lack robustness because a printed 2D barcode is very much environmentally dependent, and thus an information hiding detection scheme trained in one environment often does not work well in another environment. In this paper, we propose two hidden information detection schemes at the existing anti-copying 2D barcodes. The first scheme is to directly use the pixel distance to detect the use of an information hiding scheme in a 2D barcode, referred as to the Pixel Distance Based Detection (PDBD) scheme. The second scheme is first to calculate the variance of the raw signal and the covariance between the recovered signal and the raw signal, and then based on the variance results, detects the use of information hiding scheme in a 2D barcode, referred as to the Pixel Variance Based Detection (PVBD) scheme. Moreover, we design advanced IC attacks to evaluate the security of two existing anti-copying 2D barcodes. We implemented our schemes and conducted extensive performance comparison between our schemes and prior schemes under different capturing devices, such as a scanner and a camera phone. Our experimental results show that the PVBD scheme can correctly detect the existence of the hidden information at both the 2LQR code and the LCAC 2D barcode. Moreover, the probability of successfully attacking of our IC attacks achieves 0.6538 for the 2LQR code and 1 for the LCAC 2D barcode.

Related papers

• Addressing Detail Bottlenecks in Latent Diffusion for RGB-to-SWIR Image Translation [3.8750462550584044]
  Latent diffusion models (LDMs) enable efficient image-to-image translation but discard fine details during compression.<n>We identify two bottlenecks: the autoencoder, which loses information, and the conditioning pathway, which further degrades the source signal through naive downsampling.<n>We propose two lightweight, backbone-agnostic fixes: a Source-Conditioned Autoencoder (SCAE) that injects high-resolution source features into the decoder via skip connections, and a Learnable Guidance (LGE) that replaces naive downsampling with a learned conditioning signal.
  arXiv  Detail & Related papers  (2026-06-18T08:59:03Z)
• Power Battery Detection [91.99787495748218]
  Power batteries are essential components in electric vehicles, where internal structural defects can pose serious safety risks.<n>We conduct a comprehensive study on power battery detection (PBD), which aims to localize the dense endpoints of cathode and anode plates from X-ray images for quality inspection.<n>We present PBD5K, the first large-scale benchmark for this task, consisting of 5,000 X-ray images from nine battery types with fine-grained annotations and eight types of real-world visual interference.
  arXiv  Detail & Related papers  (2025-08-11T09:35:25Z)
• Cryptanalysis via Machine Learning Based Information Theoretic Metrics [58.96805474751668]
  We propose two novel applications of machine learning (ML) algorithms to perform cryptanalysis on any cryptosystem. These algorithms can be readily applied in an audit setting to evaluate the robustness of a cryptosystem. We show that our classification model correctly identifies the encryption schemes that are not IND-CPA secure, such as DES, RSA, and AES ECB, with high accuracy.
  arXiv  Detail & Related papers  (2025-01-25T04:53:36Z)
• Threshold Selection for Iterative Decoding of $(v,w)$-regular Binary Codes [84.0257274213152]
  Iterative bit flipping decoders are an efficient choice for sparse $(v,w)$-regular codes. We propose concrete criteria for threshold determination, backed by a closed form model.
  arXiv  Detail & Related papers  (2025-01-23T17:38:22Z)
• Supervised and Unsupervised Alignments for Spoofing Behavioral Biometrics [7.021534792043867]
  Biometric recognition systems are based on intrinsic properties of their users, usually encoded in high dimension representations called embeddings. We perform spoofing attacks on two behavioral biometric systems using a set of alignment techniques.
  arXiv  Detail & Related papers  (2024-08-14T20:46:59Z)
• Bit-flipping Decoder Failure Rate Estimation for (v,w)-regular Codes [84.0257274213152]
  We propose a new technique to provide accurate estimates of the DFR of a two-iterations (parallel) bit flipping decoder. We validate our results, providing comparisons of the modeled and simulated weight of the syndrome, incorrectly-guessed error bit distribution at the end of the first iteration, and two-itcrypteration Decoding Failure Rates (DFR)
  arXiv  Detail & Related papers  (2024-01-30T11:40:24Z)
• Deep Learning-Based Detection for Marker Codes over Insertion and Deletion Channels [5.310666534758956]
  Marker code is an effective coding scheme to protect data from insertions and deletions. It has potential applications in future storage systems, such as DNA storage and racetrack memory. We propose two CSI-agnostic detecting algorithms for marker code based on deep learning.
  arXiv  Detail & Related papers  (2024-01-02T11:13:01Z)
• S^2-Transformer for Mask-Aware Hyperspectral Image Reconstruction [48.83280067393851]
  A representative hyperspectral image acquisition procedure conducts a 3D-to-2D encoding by the coded aperture snapshot spectral imager (CASSI) Two major challenges stand in the way of a high-fidelity reconstruction: (i) To obtain 2D measurements, CASSI dislocates multiple channels by disperser-titling and squeezes them onto the same spatial region, yielding an entangled data loss. We propose a spatial-spectral (S2-) transformer architecture with a mask-aware learning strategy to tackle these challenges.
  arXiv  Detail & Related papers  (2022-09-24T19:26:46Z)
• Fusing Multiscale Texture and Residual Descriptors for Multilevel 2D Barcode Rebroadcasting Detection [23.999136417157597]
  In applications related to product authentication, an authentic 2D barcode can be illegally copied and attached to a counterfeited product in such a way to bypass the authentication scheme. We employ a proprietary 2D barcode pattern and use multimedia forensics methods to analyse the scanning and printing artefacts resulting from the copy (rebroadcasting) attack. A diverse and complementary feature set is proposed to quantify the barcode texture distortions introduced during the illegal copying process.
  arXiv  Detail & Related papers  (2022-05-16T06:26:20Z)
• Self-Supervised Person Detection in 2D Range Data using a Calibrated Camera [83.31666463259849]
  We propose a method to automatically generate training labels (called pseudo-labels) for 2D LiDAR-based person detectors. We show that self-supervised detectors, trained or fine-tuned with pseudo-labels, outperform detectors trained using manual annotations. Our method is an effective way to improve person detectors during deployment without any additional labeling effort.
  arXiv  Detail & Related papers  (2020-12-16T12:10:04Z)
• D-Unet: A Dual-encoder U-Net for Image Splicing Forgery Detection and Localization [108.8592577019391]
  Image splicing forgery detection is a global binary classification task that distinguishes the tampered and non-tampered regions by image fingerprints. We propose a novel network called dual-encoder U-Net (D-Unet) for image splicing forgery detection, which employs an unfixed encoder and a fixed encoder. In an experimental comparison study of D-Unet and state-of-the-art methods, D-Unet outperformed the other methods in image-level and pixel-level detection.
  arXiv  Detail & Related papers  (2020-12-03T10:54:02Z)
• Appliance identification using a histogram post-processing of 2D local binary patterns for smart grid applications [2.389598109913753]
  We propose a novel method to extract electrical power signatures after transforming the power signal to 2D space. An improved local binary patterns (LBP) is proposed that relies on improving the discriminative ability of conventional LBP. A comprehensive performance evaluation is performed on two different datasets.
  arXiv  Detail & Related papers  (2020-10-03T19:23:30Z)
• DR-SPAAM: A Spatial-Attention and Auto-regressive Model for Person Detection in 2D Range Data [81.06749792332641]
  We propose a person detection network which uses an alternative strategy to combine scans obtained at different times. DR-SPAAM keeps the intermediate features from the backbone network as a template and recurrently updates the template when a new scan becomes available. On the DROW dataset, our method outperforms the existing state-of-the-art, while being approximately four times faster.
  arXiv  Detail & Related papers  (2020-04-29T11:01:44Z)
• BiDet: An Efficient Binarized Object Detector [96.19708396510894]
  We propose a binarized neural network learning method called BiDet for efficient object detection. Our BiDet fully utilizes the representational capacity of the binary neural networks for object detection by redundancy removal. Our method outperforms the state-of-the-art binary neural networks by a sizable margin.
  arXiv  Detail & Related papers  (2020-03-09T08:16:16Z)
• A Novel Inspection System For Variable Data Printing Using Deep Learning [0.9176056742068814]
  We present a novel approach for inspecting variable data prints (VDP) with an ultra-low false alarm rate (0.005%) The system is based on a comparison between two images: a reference image and an image captured by low-cost scanners.
  arXiv  Detail & Related papers  (2020-01-13T15:07:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.