73 research outputs found
A Wild Manhunt for Stego Images Created by Modbile Apps.
Background: As mobile Internet and telecommunication technology develops at high speed, the digital image forensics academic community is facing a growing challenge. • Mobile applications (Apps) allow a user to easily edit/process an image for a variety of purposes. • Thanks to the improved cameras and editing apps on smartphones, the volume of images presented to digital image forensic practitioners increases every day. • Unfortunately, terrorists, spies and child pornography predators are also taking the advantage of the mobile app ecosystem to exchange illegal files and photos
Deep Convolutional Neural Network to Detect J-UNIWARD
This paper presents an empirical study on applying convolutional neural
networks (CNNs) to detecting J-UNIWARD, one of the most secure JPEG
steganographic method. Experiments guiding the architectural design of the CNNs
have been conducted on the JPEG compressed BOSSBase containing 10,000 covers of
size 512x512. Results have verified that both the pooling method and the depth
of the CNNs are critical for performance. Results have also proved that a
20-layer CNN, in general, outperforms the most sophisticated feature-based
methods, but its advantage gradually diminishes on hard-to-detect cases. To
show that the performance generalizes to large-scale databases and to different
cover sizes, one experiment has been conducted on the CLS-LOC dataset of
ImageNet containing more than one million covers cropped to unified size of
256x256. The proposed 20-layer CNN has cut the error achieved by a CNN recently
proposed for large-scale JPEG steganalysis by 35%. Source code is available via
GitHub: https://github.com/GuanshuoXu/deep_cnn_jpeg_steganalysisComment: Accepted by IH&MMSec 2017. This is a personal cop
Image Forgery Localization Based on Multi-Scale Convolutional Neural Networks
In this paper, we propose to utilize Convolutional Neural Networks (CNNs) and
the segmentation-based multi-scale analysis to locate tampered areas in digital
images. First, to deal with color input sliding windows of different scales, a
unified CNN architecture is designed. Then, we elaborately design the training
procedures of CNNs on sampled training patches. With a set of robust
multi-scale tampering detectors based on CNNs, complementary tampering
possibility maps can be generated. Last but not least, a segmentation-based
method is proposed to fuse the maps and generate the final decision map. By
exploiting the benefits of both the small-scale and large-scale analyses, the
segmentation-based multi-scale analysis can lead to a performance leap in
forgery localization of CNNs. Numerous experiments are conducted to demonstrate
the effectiveness and efficiency of our method.Comment: 7 pages, 6 figure
On possibility of using convolutional neural networks for creating universal attacks on iterative block ciphers
Исследуется возможность применения свёрточных нейронных сетей к задаче анализа стойкости итеративных блочных шифров. Предлагается новый подход к построению атак-различителей на основе свёрточной нейронной сети, обученной различать графические эквиваленты шифртекстов, полученных в режиме шифрования CTR (счётчика) после разного числа раундов, в том числе после такого, которое обеспечивает удовлетворительные статистические свойства шифртекста. По аналогии со статистическими тестами, предложенный подход позволяет создавать различители без необходимости проведения аналитического исследования каждого шифра, что даёт возможность строить универсальные различители сразу для серии шифров. Предлагается несколько схем построения универсальных атак-различителей, которые, как демонстрируется экспериментально, в ряде случаев позволяют выявлять отклонения от случайности на меньших выборках и при большем числе раундов, чем ранее известные статистические тесты
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