1,008 research outputs found
Boosting Image Forgery Detection using Resampling Features and Copy-move analysis
Realistic image forgeries involve a combination of splicing, resampling,
cloning, region removal and other methods. While resampling detection
algorithms are effective in detecting splicing and resampling, copy-move
detection algorithms excel in detecting cloning and region removal. In this
paper, we combine these complementary approaches in a way that boosts the
overall accuracy of image manipulation detection. We use the copy-move
detection method as a pre-filtering step and pass those images that are
classified as untampered to a deep learning based resampling detection
framework. Experimental results on various datasets including the 2017 NIST
Nimble Challenge Evaluation dataset comprising nearly 10,000 pristine and
tampered images shows that there is a consistent increase of 8%-10% in
detection rates, when copy-move algorithm is combined with different resampling
detection algorithms
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
Aligned and Non-Aligned Double JPEG Detection Using Convolutional Neural Networks
Due to the wide diffusion of JPEG coding standard, the image forensic
community has devoted significant attention to the development of double JPEG
(DJPEG) compression detectors through the years. The ability of detecting
whether an image has been compressed twice provides paramount information
toward image authenticity assessment. Given the trend recently gained by
convolutional neural networks (CNN) in many computer vision tasks, in this
paper we propose to use CNNs for aligned and non-aligned double JPEG
compression detection. In particular, we explore the capability of CNNs to
capture DJPEG artifacts directly from images. Results show that the proposed
CNN-based detectors achieve good performance even with small size images (i.e.,
64x64), outperforming state-of-the-art solutions, especially in the non-aligned
case. Besides, good results are also achieved in the commonly-recognized
challenging case in which the first quality factor is larger than the second
one.Comment: Submitted to Journal of Visual Communication and Image Representation
(first submission: March 20, 2017; second submission: August 2, 2017
Vers l’anti-criminalistique en images numériques via la restauration d’images
Image forensics enjoys its increasing popularity as a powerful image authentication tool, working in a blind passive way without the aid of any a priori embedded information compared to fragile image watermarking. On its opponent side, image anti-forensics attacks forensic algorithms for the future development of more trustworthy forensics. When image coding or processing is involved, we notice that image anti-forensics to some extent shares a similar goal with image restoration. Both of them aim to recover the information lost during the image degradation, yet image anti-forensics has one additional indispensable forensic undetectability requirement. In this thesis, we form a new research line for image anti-forensics, by leveraging on advanced concepts/methods from image restoration meanwhile with integrations of anti-forensic strategies/terms. Under this context, this thesis contributes on the following four aspects for JPEG compression and median filtering anti-forensics: (i) JPEG anti-forensics using Total Variation based deblocking, (ii) improved Total Variation based JPEG anti-forensics with assignment problem based perceptual DCT histogram smoothing, (iii) JPEG anti-forensics using JPEG image quality enhancement based on a sophisticated image prior model and non-parametric DCT histogram smoothing based on calibration, and (iv) median filtered image quality enhancement and anti-forensics via variational deconvolution. Experimental results demonstrate the effectiveness of the proposed anti-forensic methods with a better forensic undetectability against existing forensic detectors as well as a higher visual quality of the processed image, by comparisons with the state-of-the-art methods.La criminalistique en images numériques se développe comme un outil puissant pour l'authentification d'image, en travaillant de manière passive et aveugle sans l'aide d'informations d'authentification pré-intégrées dans l'image (contrairement au tatouage fragile d'image). En parallèle, l'anti-criminalistique se propose d'attaquer les algorithmes de criminalistique afin de maintenir une saine émulation susceptible d'aider à leur amélioration. En images numériques, l'anti-criminalistique partage quelques similitudes avec la restauration d'image : dans les deux cas, l'on souhaite approcher au mieux les informations perdues pendant un processus de dégradation d'image. Cependant, l'anti-criminalistique se doit de remplir au mieux un objectif supplémentaire, extit{i.e.} : être non détectable par la criminalistique actuelle. Dans cette thèse, nous proposons une nouvelle piste de recherche pour la criminalistique en images numériques, en tirant profit des concepts/méthodes avancés de la restauration d'image mais en intégrant des stratégies/termes spécifiquement anti-criminalistiques. Dans ce contexte, cette thèse apporte des contributions sur quatre aspects concernant, en criminalistique JPEG, (i) l'introduction du déblocage basé sur la variation totale pour contrer les méthodes de criminalistique JPEG et (ii) l'amélioration apportée par l'adjonction d'un lissage perceptuel de l'histogramme DCT, (iii) l'utilisation d'un modèle d'image sophistiqué et d'un lissage non paramétrique de l'histogramme DCT visant l'amélioration de la qualité de l'image falsifiée; et, en criminalistique du filtrage médian, (iv) l'introduction d'une méthode fondée sur la déconvolution variationnelle. Les résultats expérimentaux démontrent l'efficacité des méthodes anti-criminalistiques proposées, avec notamment une meilleure indétectabilité face aux détecteurs criminalistiques actuels ainsi qu'une meilleure qualité visuelle de l'image falsifiée par rapport aux méthodes anti-criminalistiques de l'état de l'art
Universal Adversarial Perturbations Through the Lens of Deep Steganography: Towards A Fourier Perspective
The booming interest in adversarial attacks stems from a misalignment between
human vision and a deep neural network (DNN), i.e. a human imperceptible
perturbation fools the DNN. Moreover, a single perturbation, often called
universal adversarial perturbation (UAP), can be generated to fool the DNN for
most images. A similar misalignment phenomenon has recently also been observed
in the deep steganography task, where a decoder network can retrieve a secret
image back from a slightly perturbed cover image. We attempt explaining the
success of both in a unified manner from the Fourier perspective. We perform
task-specific and joint analysis and reveal that (a) frequency is a key factor
that influences their performance based on the proposed entropy metric for
quantifying the frequency distribution; (b) their success can be attributed to
a DNN being highly sensitive to high-frequency content. We also perform feature
layer analysis for providing deep insight on model generalization and
robustness. Additionally, we propose two new variants of universal
perturbations: (1) Universal Secret Adversarial Perturbation (USAP) that
simultaneously achieves attack and hiding; (2) high-pass UAP (HP-UAP) that is
less visible to the human eye.Comment: Accepted to AAAI 202
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