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Found 19 papers, 6 papers with code
Hierarchical Invariance for Robust and Interpretable Vision Tasks at Larger Scales
Developing robust and interpretable vision systems is a crucial step towards trustworthy artificial intelligence.
Turn Passive to Active: A Survey on Active Intellectual Property Protection of Deep Learning Models
In this review, we attempt to clearly elaborate on the connotation, attributes, and requirements of active DNN copyright protection, provide evaluation methods and metrics for active copyright protection, review and analyze existing work on active DL model intellectual property protection, discuss potential attacks that active DL model copyright protection techniques may face, and provide challenges and future directions for active DL model intellectual property protection.
Seeing is not Believing: An Identity Hider for Human Vision Privacy Protection
Massive captured face images are stored in the database for the identification of individuals.
Towards an Accurate and Secure Detector against Adversarial Perturbations
It expands the above works on two aspects: 1) the introduced Krawtchouk basis provides better spatial-frequency discriminability and thereby is more suitable for capturing adversarial patterns than the common trigonometric or wavelet basis; 2) the extensive parameters for decomposition are generated by a pseudo-random function with secret keys, hence blocking the defense-aware adversarial attack.
Representing Noisy Image Without Denoising
In this paper, we explore a non-learning paradigm that aims to derive robust representation directly from noisy images, without the denoising as pre-processing.
InFIP: An Explainable DNN Intellectual Property Protection Method based on Intrinsic Features
Since the intrinsic feature is composed of unique interpretation of the model's decision, the intrinsic feature can be regarded as fingerprint of the model.
Uformer-ICS: A Specialized U-Shaped Transformer for Image Compressive Sensing
We develop a projection-based transformer block by integrating the prior projection knowledge of CS into the original transformer blocks, and then build a symmetrical reconstruction model using the projection-based transformer blocks and residual convolutional blocks.
Shrinking the Semantic Gap: Spatial Pooling of Local Moment Invariants for Copy-Move Forgery Detection
In this paper, we present a very first study of trying to mitigate the semantic gap problem in copy-move forgery detection, with spatial pooling of local moment invariants for midlevel image representation.
Detecting Recolored Image by Spatial Correlation
In this paper, we try to explore a solution from the perspective of the spatial correlation, which exhibits the generic detection capability for both conventional and deep learning-based recoloring.
A Principled Design of Image Representation: Towards Forensic Tasks
Image forensics is a rising topic as the trustworthy multimedia content is critical for modern society.
Imperceptible and Multi-channel Backdoor Attack against Deep Neural Networks
Recent researches demonstrate that Deep Neural Networks (DNN) models are vulnerable to backdoor attacks.
Compression-Resistant Backdoor Attack against Deep Neural Networks
After training, the backdoor attack against DNN is robust to image compression.
Detect and remove watermark in deep neural networks via generative adversarial networks
Experimental evaluations on the MNIST and CIFAR10 datasets demonstrate that, the proposed method can effectively remove about 98% of the watermark in DNN models, as the watermark retention rate reduces from 100% to less than 2% after applying the proposed attack.
Detecting Backdoor in Deep Neural Networks via Intentional Adversarial Perturbations
Experimental results show that, the backdoor detection rate of the proposed defense method is 99. 63%, 99. 76% and 99. 91% on Fashion-MNIST, CIFAR-10 and GTSRB datasets, respectively.
AdvParams: An Active DNN Intellectual Property Protection Technique via Adversarial Perturbation Based Parameter Encryption
Moreover, the proposed method only needs to encrypt an extremely low number of parameters, and the proportion of the encrypted parameters of all the model's parameters is as low as 0. 000205%.
A Survey of Orthogonal Moments for Image Representation: Theory, Implementation, and Evaluation
Image representation is an important topic in computer vision and pattern recognition.
ActiveGuard: An Active DNN IP Protection Technique via Adversarial Examples
For ownership verification, the embedded watermark can be successfully extracted, while the normal performance of the DNN model will not be affected.
A multi-level approach with visual information for encrypted H.265/HEVC videos
High-efficiency video coding (HEVC) encryption has been proposed to encrypt syntax elements for the purpose of video encryption.
Indoor image representation by high-level semantic features
Nevertheless, most of the existing feature extraction methods, which extract features based on pixels, color, shape/object parts or objects on images, suffer from limited capabilities in describing semantic information (e. g., object association).
