Visible to the public Biblio

Found 224 results

Filters: Keyword is Support vector machines  [Clear All Filters]
2021-04-08
Boato, G., Dang-Nguyen, D., Natale, F. G. B. De.  2020.  Morphological Filter Detector for Image Forensics Applications. IEEE Access. 8:13549—13560.
Mathematical morphology provides a large set of powerful non-linear image operators, widely used for feature extraction, noise removal or image enhancement. Although morphological filters might be used to remove artifacts produced by image manipulations, both on binary and gray level documents, little effort has been spent towards their forensic identification. In this paper we propose a non-trivial extension of a deterministic approach originally detecting erosion and dilation of binary images. The proposed approach operates on grayscale images and is robust to image compression and other typical attacks. When the image is attacked the method looses its deterministic nature and uses a properly trained SVM classifier, using the original detector as a feature extractor. Extensive tests demonstrate that the proposed method guarantees very high accuracy in filtering detection, providing 100% accuracy in discriminating the presence and the type of morphological filter in raw images of three different datasets. The achieved accuracy is also good after JPEG compression, equal or above 76.8% on all datasets for quality factors above 80. The proposed approach is also able to determine the adopted structuring element for moderate compression factors. Finally, it is robust against noise addition and it can distinguish morphological filter from other filters.
2021-03-29
Oğuz, K., Korkmaz, İ, Korkmaz, B., Akkaya, G., Alıcı, C., Kılıç, E..  2020.  Effect of Age and Gender on Facial Emotion Recognition. 2020 Innovations in Intelligent Systems and Applications Conference (ASYU). :1—6.

New research fields and applications on human computer interaction will emerge based on the recognition of emotions on faces. With such aim, our study evaluates the features extracted from faces to recognize emotions. To increase the success rate of these features, we have run several tests to demonstrate how age and gender affect the results. The artificial neural networks were trained by the apparent regions on the face such as eyes, eyebrows, nose, mouth, and jawline and then the networks are tested with different age and gender groups. According to the results, faces of older people have a lower performance rate of emotion recognition. Then, age and gender based groups are created manually, and we show that performance rates of facial emotion recognition have increased for the networks that are trained using these particular groups.

Jia, C., Li, C. L., Ying, Z..  2020.  Facial expression recognition based on the ensemble learning of CNNs. 2020 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC). :1—5.

As a part of body language, facial expression is a psychological state that reflects the current emotional state of the person. Recognition of facial expressions can help to understand others and enhance communication with others. We propose a facial expression recognition method based on convolutional neural network ensemble learning in this paper. Our model is composed of three sub-networks, and uses the SVM classifier to Integrate the output of the three networks to get the final result. The recognition accuracy of the model's expression on the FER2013 dataset reached 71.27%. The results show that the method has high test accuracy and short prediction time, and can realize real-time, high-performance facial recognition.

Xu, X., Ruan, Z., Yang, L..  2020.  Facial Expression Recognition Based on Graph Neural Network. 2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC). :211—214.

Facial expressions are one of the most powerful, natural and immediate means for human being to present their emotions and intensions. In this paper, we present a novel method for fully automatic facial expression recognition. The facial landmarks are detected for characterizing facial expressions. A graph convolutional neural network is proposed for feature extraction and facial expression recognition classification. The experiments were performed on the three facial expression databases. The result shows that the proposed FER method can achieve good recognition accuracy up to 95.85% using the proposed method.

Olaimat, M. Al, Lee, D., Kim, Y., Kim, J., Kim, J..  2020.  A Learning-based Data Augmentation for Network Anomaly Detection. 2020 29th International Conference on Computer Communications and Networks (ICCCN). :1–10.
While machine learning technologies have been remarkably advanced over the past several years, one of the fundamental requirements for the success of learning-based approaches would be the availability of high-quality data that thoroughly represent individual classes in a problem space. Unfortunately, it is not uncommon to observe a significant degree of class imbalance with only a few instances for minority classes in many datasets, including network traffic traces highly skewed toward a large number of normal connections while very small in quantity for attack instances. A well-known approach to addressing the class imbalance problem is data augmentation that generates synthetic instances belonging to minority classes. However, traditional statistical techniques may be limited since the extended data through statistical sampling should have the same density as original data instances with a minor degree of variation. This paper takes a learning-based approach to data augmentation to enable effective network anomaly detection. One of the critical challenges for the learning-based approach is the mode collapse problem resulting in a limited diversity of samples, which was also observed from our preliminary experimental result. To this end, we present a novel "Divide-Augment-Combine" (DAC) strategy, which groups the instances based on their characteristics and augments data on a group basis to represent a subset independently using a generative adversarial model. Our experimental results conducted with two recently collected public network datasets (UNSW-NB15 and IDS-2017) show that the proposed technique enhances performances up to 21.5% for identifying network anomalies.
2021-03-18
Kirkbride, P., Dewan, M. A. Akber, Lin, F..  2020.  Game-Like Captchas for Intrusion Detection. 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). :312—315.

In this paper, we consider a novel method of mining biometric data for user authentication by replacing traditional captchas with game-like captchas. The game-like captchas present the user with a short game in which they attempt to get a high score. The data produced from a user's game play will be used to produce a behavior biometric based on user interactions, such as mouse movement, click patterns and game choices. The baseline expectation of interactive behavior will be used as a single factor in an intrusion detection system providing continuous authentication, considering the factors such as IP address, location, time of use, website interactions, and behavior anomalies. In addition to acting as a source of data, game-like captchas are expected to deter bots and automated systems from accessing web-based services and improving the user experience for the end-users who have become accustomed to monotonous alternatives, such as Google's re-captcha.

2021-03-09
Muhammad, A., Asad, M., Javed, A. R..  2020.  Robust Early Stage Botnet Detection using Machine Learning. 2020 International Conference on Cyber Warfare and Security (ICCWS). :1—6.

Among the different types of malware, botnets are rising as the most genuine risk against cybersecurity as they give a stage to criminal operations (e.g., Distributed Denial of Service (DDOS) attacks, malware dispersal, phishing, and click fraud and identity theft). Existing botnet detection techniques work only on specific botnet Command and Control (C&C) protocols and lack in providing early-stage botnet detection. In this paper, we propose an approach for early-stage botnet detection. The proposed approach first selects the optimal features using feature selection techniques. Next, it feeds these features to machine learning classifiers to evaluate the performance of the botnet detection. Experiments reveals that the proposed approach efficiently classifies normal and malicious traffic at an early stage. The proposed approach achieves the accuracy of 99%, True Positive Rate (TPR) of 0.99 %, and False Positive Rate (FPR) of 0.007 % and provide an efficient detection rate in comparison with the existing approach.

Guibene, K., Ayaida, M., Khoukhi, L., MESSAI, N..  2020.  Black-box System Identification of CPS Protected by a Watermark-based Detector. 2020 IEEE 45th Conference on Local Computer Networks (LCN). :341–344.
The implication of Cyber-Physical Systems (CPS) in critical infrastructures (e.g., smart grids, water distribution networks, etc.) has introduced new security issues and vulnerabilities to those systems. In this paper, we demonstrate that black-box system identification using Support Vector Regression (SVR) can be used efficiently to build a model of a given industrial system even when this system is protected with a watermark-based detector. First, we briefly describe the Tennessee Eastman Process used in this study. Then, we present the principal of detection scheme and the theory behind SVR. Finally, we design an efficient black-box SVR algorithm for the Tennessee Eastman Process. Extensive simulations prove the efficiency of our proposed algorithm.
2021-03-01
Kerim, A., Genc, B..  2020.  Mobile Games Success and Failure: Mining the Hidden Factors. 2020 7th International Conference on Soft Computing Machine Intelligence (ISCMI). :167–171.
Predicting the success of a mobile game is a prime issue in game industry. Thousands of games are being released each day. However, a few of them succeed while the majority fail. Towards the goal of investigating the potential correlation between the success of a mobile game and its specific attributes, this work was conducted. More than 17 thousands games were considered for that reason. We show that specific game attributes, such as number of IAPs (In-App Purchases), belonging to the puzzle genre, supporting different languages and being produced by a mature developer highly and positively affect the success of the game in the future. Moreover, we show that releasing the game in July and not including any IAPs seems to be highly associated with the game’s failure. Our second main contribution, is the proposal of a novel success score metric that reflects multiple objectives, in contrast to evaluating only revenue, average rating or rating count. We also employ different machine learning models, namely, SVM (Support Vector Machine), RF (Random Forest) and Deep Learning (DL) to predict this success score metric of a mobile game given its attributes. The trained models were able to predict this score, as well as the rating average and rating count of a mobile game with more than 70% accuracy. This prediction can help developers before releasing their game to the market to avoid any potential disappointments.
Perisetty, A., Bodempudi, S. T., Shaik, P. Rahaman, Kumar, B. L. N. Phaneendra.  2020.  Classification of Hyperspectral Images using Edge Preserving Filter and Nonlinear Support Vector Machine (SVM). 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS). :1050–1054.
Hyperspectral image is acquired with a special sensor in which the information is collected continuously. This sensor will provide abundant data from the scene captured. The high voluminous data in this image give rise to the extraction of materials and other valuable items in it. This paper proposes a methodology to extract rich information from the hyperspectral images. As the information collected in a contiguous manner, there is a need to extract spectral bands that are uncorrelated. A factor analysis based dimensionality reduction technique is employed to extract the spectral bands and a weight least square filter is used to get the spatial information from the data. Due to the preservation of edge property in the spatial filter, much information is extracted during the feature extraction phase. Finally, a nonlinear SVM is applied to assign a class label to the pixels in the image. The research work is tested on the standard dataset Indian Pines. The performance of the proposed method on this dataset is assessed through various accuracy measures. These accuracies are 96%, 92.6%, and 95.4%. over the other methods. This methodology can be applied to forestry applications to extract the various metrics in the real world.
Lim, S., Ko, Y..  2020.  Intellectual Priority-based Low Latency Data Delivery Scheme for Multi-interface and Multi-channel Devices in Multi-hop Wireless Mesh Networks. 2020 IEEE International Conference on Big Data and Smart Computing (BigComp). :417–419.
In multi-hop wireless mesh networks, the end-to-end delay for a packet is getting longer as the relaying hops to the destination are increasing. The real-time packet such as the urgent safety message should be delivered within the stipulated deadline. Most previous studies have been focused to find out the optimal route to the destination. We propose an intellectual priority-based packet transmission scheme for multi-interface devices in multi-hop wireless mesh networks.
2021-02-23
Ashraf, S., Ahmed, T..  2020.  Sagacious Intrusion Detection Strategy in Sensor Network. 2020 International Conference on UK-China Emerging Technologies (UCET). :1—4.
Almost all smart appliances are operated through wireless sensor networks. With the passage of time, due to various applications, the WSN becomes prone to various external attacks. Preventing such attacks, Intrusion Detection strategy (IDS) is very crucial to secure the network from the malicious attackers. The proposed IDS methodology discovers the pattern in large data corpus which works for different types of algorithms to detect four types of Denial of service (DoS) attacks, namely, Grayhole, Blackhole, Flooding, and TDMA. The state-of-the-art detection algorithms, such as KNN, Naïve Bayes, Logistic Regression, Support Vector Machine (SVM), and ANN are applied to the data corpus and analyze the performance in detecting the attacks. The analysis shows that these algorithms are applicable for the detection and prediction of unavoidable attacks and can be recommended for network experts and analysts.
Chen, W., Cao, H., Lv, X., Cao, Y..  2020.  A Hybrid Feature Extraction Network for Intrusion Detection Based on Global Attention Mechanism. 2020 International Conference on Computer Information and Big Data Applications (CIBDA). :481—485.
The widespread application of 5G will make intrusion detection of large-scale network traffic a mere need. However, traditional intrusion detection cannot meet the requirements by manually extracting features, and the existing AI methods are also relatively inefficient. Therefore, when performing intrusion detection tasks, they have significant disadvantages of high false alarm rates and low recognition performance. For this challenge, this paper proposes a novel hybrid network, RULA-IDS, which can perform intrusion detection tasks by great amount statistical data from the network monitoring system. RULA-IDS consists of the fully connected layer, the feature extraction layer, the global attention mechanism layer and the SVM classification layer. In the feature extraction layer, the residual U-Net and LSTM are used to extract the spatial and temporal features of the network traffic attributes. It is worth noting that we modified the structure of U-Net to suit the intrusion detection task. The global attention mechanism layer is then used to selectively retain important information from a large number of features and focus on those. Finally, the SVM is used as a classifier to output results. The experimental results show that our method outperforms existing state-of-the-art intrusion detection methods, and the accuracies of training and testing are improved to 97.01% and 98.19%, respectively, and presents stronger robustness during training and testing.
2021-02-22
Haile, J., Havens, S..  2020.  Identifying Ubiquitious Third-Party Libraries in Compiled Executables Using Annotated and Translated Disassembled Code with Supervised Machine Learning. 2020 IEEE Security and Privacy Workshops (SPW). :157–162.
The size and complexity of the software ecosystem is a major challenge for vendors, asset owners and cybersecurity professionals who need to understand the security posture of these systems. Annotated and Translated Disassembled Code is a graph based datastore designed to organize firmware and software analysis data across builds, packages and systems, providing a highly scalable platform enabling automated binary software analysis tasks including corpora construction and storage for machine learning. This paper describes an approach for the identification of ubiquitous third-party libraries in firmware and software using Annotated and Translated Disassembled Code and supervised machine learning. Annotated and Translated Disassembled Code provide matched libraries, function names and addresses of previously unidentified code in software as it is being automatically analyzed. This data can be ingested by other software analysis tools to improve accuracy and save time. Defenders can add the identified libraries to their vulnerability searches and add effective detection and mitigation into their operating environment.
2021-02-16
Başkaya, D., Samet, R..  2020.  DDoS Attacks Detection by Using Machine Learning Methods on Online Systems. 2020 5th International Conference on Computer Science and Engineering (UBMK). :52—57.
DDoS attacks impose serious threats to many large or small organizations; therefore DDoS attacks have to be detected as soon as possible. In this study, a methodology to detect DDoS attacks is proposed and implemented on online systems. In the scope of the proposed methodology, Multi Layer Perceptron (MLP), Random Forest (RF), K-Nearest Neighbor (KNN), C-Support Vector Machine (SVC) machine learning methods are used with scaling and feature reduction preprocessing methods and then effects of preprocesses on detection accuracy rates of HTTP (Hypertext Transfer Protocol) flood, TCP SYN (Transport Control Protocol Synchronize) flood, UDP (User Datagram Protocol) flood and ICMP (Internet Control Message Protocol) flood DDoS attacks are analyzed. Obtained results showed that DDoS attacks can be detected with high accuracy of 99.2%.
2021-02-01
Bai, Y., Guo, Y., Wei, J., Lu, L., Wang, R., Wang, Y..  2020.  Fake Generated Painting Detection Via Frequency Analysis. 2020 IEEE International Conference on Image Processing (ICIP). :1256–1260.
With the development of deep neural networks, digital fake paintings can be generated by various style transfer algorithms. To detect the fake generated paintings, we analyze the fake generated and real paintings in Fourier frequency domain and observe statistical differences and artifacts. Based on our observations, we propose Fake Generated Painting Detection via Frequency Analysis (FGPD-FA) by extracting three types of features in frequency domain. Besides, we also propose a digital fake painting detection database for assessing the proposed method. Experimental results demonstrate the excellence of the proposed method in different testing conditions.
2021-01-20
Rashid, A., Siddique, M. J., Ahmed, S. M..  2020.  Machine and Deep Learning Based Comparative Analysis Using Hybrid Approaches for Intrusion Detection System. 2020 3rd International Conference on Advancements in Computational Sciences (ICACS). :1—9.

Intrusion detection is one of the most prominent and challenging problem faced by cybersecurity organizations. Intrusion Detection System (IDS) plays a vital role in identifying network security threats. It protects the network for vulnerable source code, viruses, worms and unauthorized intruders for many intranet/internet applications. Despite many open source APIs and tools for intrusion detection, there are still many network security problems exist. These problems are handled through the proper pre-processing, normalization, feature selection and ranking on benchmark dataset attributes prior to the enforcement of self-learning-based classification algorithms. In this paper, we have performed a comprehensive comparative analysis of the benchmark datasets NSL-KDD and CIDDS-001. For getting optimal results, we have used the hybrid feature selection and ranking methods before applying self-learning (Machine / Deep Learning) classification algorithmic approaches such as SVM, Naïve Bayes, k-NN, Neural Networks, DNN and DAE. We have analyzed the performance of IDS through some prominent performance indicator metrics such as Accuracy, Precision, Recall and F1-Score. The experimental results show that k-NN, SVM, NN and DNN classifiers perform approx. 100% accuracy regarding performance evaluation metrics on the NSL-KDD dataset whereas k-NN and Naïve Bayes classifiers perform approx. 99% accuracy on the CIDDS-001 dataset.

Lei, M., Jin, M., Huang, T., Guo, Z., Wang, Q., Wu, Z., Chen, Z., Chen, X., Zhang, J..  2020.  Ultra-wideband Fingerprinting Positioning Based on Convolutional Neural Network. 2020 International Conference on Computer, Information and Telecommunication Systems (CITS). :1—5.

The Global Positioning System (GPS) can determine the position of any person or object on earth based on satellite signals. But when inside the building, the GPS cannot receive signals, the indoor positioning system will determine the precise position. How to achieve more precise positioning is the difficulty of an indoor positioning system now. In this paper, we proposed an ultra-wideband fingerprinting positioning method based on a convolutional neural network (CNN), and we collect the dataset in a room to test the model, then compare our method with the existing method. In the experiment, our method can reach an accuracy of 98.36%. Compared with other fingerprint positioning methods our method has a great improvement in robustness. That results show that our method has good practicality while achieves higher accuracy.

2021-01-15
McCloskey, S., Albright, M..  2019.  Detecting GAN-Generated Imagery Using Saturation Cues. 2019 IEEE International Conference on Image Processing (ICIP). :4584—4588.
Image forensics is an increasingly relevant problem, as it can potentially address online disinformation campaigns and mitigate problematic aspects of social media. Of particular interest, given its recent successes, is the detection of imagery produced by Generative Adversarial Networks (GANs), e.g. `deepfakes'. Leveraging large training sets and extensive computing resources, recent GANs can be trained to generate synthetic imagery which is (in some ways) indistinguishable from real imagery. We analyze the structure of the generating network of a popular GAN implementation [1], and show that the network's treatment of exposure is markedly different from a real camera. We further show that this cue can be used to distinguish GAN-generated imagery from camera imagery, including effective discrimination between GAN imagery and real camera images used to train the GAN.
Yang, X., Li, Y., Lyu, S..  2019.  Exposing Deep Fakes Using Inconsistent Head Poses. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :8261—8265.
In this paper, we propose a new method to expose AI-generated fake face images or videos (commonly known as the Deep Fakes). Our method is based on the observations that Deep Fakes are created by splicing synthesized face region into the original image, and in doing so, introducing errors that can be revealed when 3D head poses are estimated from the face images. We perform experiments to demonstrate this phenomenon and further develop a classification method based on this cue. Using features based on this cue, an SVM classifier is evaluated using a set of real face images and Deep Fakes.
Kharbat, F. F., Elamsy, T., Mahmoud, A., Abdullah, R..  2019.  Image Feature Detectors for Deepfake Video Detection. 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA). :1—4.
Detecting DeepFake videos are one of the challenges in digital media forensics. This paper proposes a method to detect deepfake videos using Support Vector Machine (SVM) regression. The SVM classifier can be trained with feature points extracted using one of the different feature-point detectors such as HOG, ORB, BRISK, KAZE, SURF, and FAST algorithms. A comprehensive test of the proposed method is conducted using a dataset of original and fake videos from the literature. Different feature point detectors are tested. The result shows that the proposed method of using feature-detector-descriptors for training the SVM can be effectively used to detect false videos.
Khalid, H., Woo, S. S..  2020.  OC-FakeDect: Classifying Deepfakes Using One-class Variational Autoencoder. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). :2794—2803.
An image forgery method called Deepfakes can cause security and privacy issues by changing the identity of a person in a photo through the replacement of his/her face with a computer-generated image or another person's face. Therefore, a new challenge of detecting Deepfakes arises to protect individuals from potential misuses. Many researchers have proposed various binary-classification based detection approaches to detect deepfakes. However, binary-classification based methods generally require a large amount of both real and fake face images for training, and it is challenging to collect sufficient fake images data in advance. Besides, when new deepfakes generation methods are introduced, little deepfakes data will be available, and the detection performance may be mediocre. To overcome these data scarcity limitations, we formulate deepfakes detection as a one-class anomaly detection problem. We propose OC-FakeDect, which uses a one-class Variational Autoencoder (VAE) to train only on real face images and detects non-real images such as deepfakes by treating them as anomalies. Our preliminary result shows that our one class-based approach can be promising when detecting Deepfakes, achieving a 97.5% accuracy on the NeuralTextures data of the well-known FaceForensics++ benchmark dataset without using any fake images for the training process.
2021-01-11
Wu, N., Farokhi, F., Smith, D., Kaafar, M. A..  2020.  The Value of Collaboration in Convex Machine Learning with Differential Privacy. 2020 IEEE Symposium on Security and Privacy (SP). :304–317.
In this paper, we apply machine learning to distributed private data owned by multiple data owners, entities with access to non-overlapping training datasets. We use noisy, differentially-private gradients to minimize the fitness cost of the machine learning model using stochastic gradient descent. We quantify the quality of the trained model, using the fitness cost, as a function of privacy budget and size of the distributed datasets to capture the trade-off between privacy and utility in machine learning. This way, we can predict the outcome of collaboration among privacy-aware data owners prior to executing potentially computationally-expensive machine learning algorithms. Particularly, we show that the difference between the fitness of the trained machine learning model using differentially-private gradient queries and the fitness of the trained machine model in the absence of any privacy concerns is inversely proportional to the size of the training datasets squared and the privacy budget squared. We successfully validate the performance prediction with the actual performance of the proposed privacy-aware learning algorithms, applied to: financial datasets for determining interest rates of loans using regression; and detecting credit card frauds using support vector machines.
Zhao, F., Skums, P., Zelikovsky, A., Sevigny, E. L., Swahn, M. H., Strasser, S. M., Huang, Y., Wu, Y..  2020.  Computational Approaches to Detect Illicit Drug Ads and Find Vendor Communities Within Social Media Platforms. IEEE/ACM Transactions on Computational Biology and Bioinformatics. :1–1.
The opioid abuse epidemic represents a major public health threat to global populations. The role social media may play in facilitating illicit drug trade is largely unknown due to limited research. However, it is known that social media use among adults in the US is widespread, there is vast capability for online promotion of illegal drugs with delayed or limited deterrence of such messaging, and further, general commercial sale applications provide safeguards for transactions; however, they do not discriminate between legal and illegal sale transactions. These characteristics of the social media environment present challenges to surveillance which is needed for advancing knowledge of online drug markets and the role they play in the drug abuse and overdose deaths. In this paper, we present a computational framework developed to automatically detect illicit drug ads and communities of vendors.The SVM- and CNNbased methods for detecting illicit drug ads, and a matrix factorization based method for discovering overlapping communities have been extensively validated on the large dataset collected from Google+, Flickr and Tumblr. Pilot test results demonstrate that our computational methods can effectively identify illicit drug ads and detect vendor-community with accuracy. These methods hold promise to advance scientific knowledge surrounding the role social media may play in perpetuating the drug abuse epidemic.
2020-12-28
Barni, M., Nowroozi, E., Tondi, B., Zhang, B..  2020.  Effectiveness of Random Deep Feature Selection for Securing Image Manipulation Detectors Against Adversarial Examples. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :2977—2981.

We investigate if the random feature selection approach proposed in [1] to improve the robustness of forensic detectors to targeted attacks, can be extended to detectors based on deep learning features. In particular, we study the transferability of adversarial examples targeting an original CNN image manipulation detector to other detectors (a fully connected neural network and a linear SVM) that rely on a random subset of the features extracted from the flatten layer of the original network. The results we got by considering three image manipulation detection tasks (resizing, median filtering and adaptive histogram equalization), two original network architectures and three classes of attacks, show that feature randomization helps to hinder attack transferability, even if, in some cases, simply changing the architecture of the detector, or even retraining the detector is enough to prevent the transferability of the attacks.