Deepfakes leverage artificial intelligence to generate highly realistic but falsified visual content, raising concerns for security and trust in digital media. Detecting such manipulations becomes more challenging when videos are compressed, as compression algorithms introduce artifacts that obscure forensic evidence. One possible solution is to train separate models for different compression levels; however, this approach increases computational costs and limits scalability. To address this challenge, we introduce a unified framework designed to improve robustness against varying degrees of video compression. Our approach combines (i) a dedicated MPEG-based augmentation strategy tailored for compressed videos, and (ii) two architectural designs named Multi-Head (MHN) and the Multi-Branch Network (MBN). The MHN extends a standard backbone by appending lightweight output layers, or ”heads”, that jointly predict deepfake likelihood and compression level, enabling compression-aware detection with minimal architectural changes. The MBN combines multiple MHNs into a modular, parallel architecture, offering an alternative to conventional depth-based model scaling. Experiments on the FaceForensics++ and Celeb-DF datasets show that both MHN and MBN improve detection performance in compressed scenarios. Notably, MHN applied to a lightweight backbone outperforms deeper and more complex models without the multi-head extension, making the proposed solution well-suited for deployment in resource-constrained settings.

The rapid proliferation of AI-generated media, particularly hyper-realistic deepfakes, has underscored the critical need for robust detection systems to mitigate risks such as misinformation and identity theft. However, state-of-the-art deepfake detectors remain vulnerable to adversarial attacks-subtle perturbations designed to evade classification. To address this gap, we organized the Adversarial Attacks on Deepfake Detectors (AADD-2025) challenge, a competitive evaluation aimed at advancing methodologies to expose and strengthen weaknesses in deepfake detection models. The challenge tasked participants with generating adversarial examples capable of evading four diverse classifiers (including ResNet, DenseNet, and two blind models) while preserving structural similarity to original deepfakes. A dataset comprising 16 subsets of high- and low-quality deepfake images generated by GAN-based and diffusion models (e.g., StableDiffusion, StyleGAN3) was provided. Participants were evaluated using a weighted combination of Structural Similarity Index (SSIM) and attack success rates across all classifiers. Thirteen teams proposed innovative solutions leveraging techniques such as latent-space manipulation, ensemble gradient optimization, surrogate modeling, and frequency-domain perturbation. Top-performing approaches, including MR-CAS (1st place), Safe AI (2nd place), and RoMa (3rd place), achieved high SSIM scores (0.74-0.93) while successfully misleading classifiers. Notably, MR-CAS's latent diffusion model inversion strategy and Safe AI's consensus-orthogonal gradient weighting framework demonstrated superior transferability across architectures, including Vision Transformers. The challenge revealed critical insights: latent-space attacks outperformed pixel-level methods, ensemble-based strategies enhanced cross-model robustness, and adversarial perturbations optimized for both CNNs and transformers proved most effective. However, gaps persist in generalizing attacks across heterogeneous models and maintaining perceptual fidelity, highlighting the urgency of developing adaptive defenses and hybrid detection mechanisms. By fostering collaboration and innovation, AADD-2025 provides a benchmark for evaluating adversarial robustness in deepfake detection and underscores the need for resilient systems in the era of AI-generated media.

The proliferation of generative models, particularly Generative Adversarial Networks (GANs) and Diffusion Models, has reshaped multimedia content creation. Alongside creative and commercial opportunities, they have introduced unprecedented risks through the production of highly realistic synthetic content, or deepfakes. These artifacts challenge visual and auditory trust, with major implications for media, security, politics, and law. This workshop provides a forum to examine deepfake technology from forensic, technical, legal, and social perspectives. It will bring together experts to advance robust and explainable detection methods, define benchmarking practices, and address ethical and regulatory frameworks. Topics include detection and attribution, adversarial countermeasures, multimodal analysis, model traceability, legal admissibility of synthetic content, as well as real-world deployment challenges and dataset creation. Further information about the workshop is available at https://iplab.dmi.unict.it/mfs/acm-dff-ws-2025/

When deepfakes are widespread on chatting platforms, they are expected to be subject to heavy resizing and compressing steps. In this paper, we present a tensor-based representation of compressed and resized images. Tensor embeds DCT features computed on multi-scaled and multi-compressed versions of the input facial image. Moreover, a custom deep-architecture is designed and trained on the proposed representation. Experimental results show its pros and cons with respect to state-of-the-art methods.

The estimation of the misalignment between consecutive JPEG compressions is a really important task that can be useful in forensics investigation for first quantization matrix estimation and forgery localization. Based on the analysis of DCT histograms obtained applying a third compression, an effective shift estimation solution has been designed. More-over, to increase the overall robustness in challenging conditions (e.g., small patches) several fusion strategies combining all the DCT coefficient information have been investigated. Finally, the effectiveness of the proposed solution has been demonstrated considering several scenarios (i.e., different patch sizes and quantization matrices) and comparisons with state-of-the-art solutions.

Multiple JPEG compressions leave artifacts in digital images: residual traces that could be exploited in forensics investigations to recover information about the device employed for acquisition or image editing software. In this paper, a novel First Quantization Estimation (FQE) algorithm based on convolutional neural networks (CNNs) is proposed. In particular, a solution based on an ensemble of CNNs was developed in conjunction with specific regularization strategies exploiting assumptions about neighboring element values of the quantization matrix to be inferred. Mostly designed to work in the aligned case, the solution was tested in challenging scenarios involving different input patch sizes, quantization matrices (both standard and custom) and datasets (i.e., RAISE and UCID collections). Comparisons with state-of-the-art solutions confirmed the effectiveness of the presented solution demonstrating for the first time to cover the widest combinations of parameters of double JPEG compressions.

Abstract The JPEG compression algorithm has proven to be efficient in saving storage and preserving image quality thus becoming extremely popular. On the other hand, the overall process leaves traces into encoded signals which are typically exploited for forensic purposes: for instance, the compression parameters of the acquisition device (or editing software) could be inferred. To this aim, in this paper a novel technique to estimate “previous” JPEG quantization factors on images compressed multiple times, in the aligned case by analyzing statistical traces hidden on Discrete Cosine Transform (DCT) histograms is exploited. Experimental results on double, triple and quadruple compressed images, demonstrate the effectiveness of the proposed technique while unveiling further interesting insights.

Multimedia Forensics experts work consists in providing answers about integrity of a specific media content and from where it comes from. Exploitation of any traces from JPEG double compressed images is often one of the main investigative path to be used for these purposes. Thus it is fundamental to have tools and algorithms able to safely estimate the first quantization matrix to further proceed with camera model identification and related tasks. In this paper, a technique based on extensive simulation is proposed, with the aim to infer the first quantization for a certain numbers of Discrete Cosine Transform (DCT) coefficients exploiting local image statistics without using any a-priori knowledge. The method provides also a reliable confidence value for the estimation which is of great importance for forensic purposes. Experimental results w.r.t. the state-of-the-art demonstrate the effectiveness of the proposed technique both in terms of precision and overall reliability.

Feature extractors play an important role in different Computer Vision application domains such as registration, recognition and visual search. Different detectors have been proposed and evaluated so far assuming images taken with classic cameras. However, many operating cameras (e.g., in surveillance and automotive) are built considering a fisheye model and a preprocessing step is performed to remove the distortion of the images before running a detector. The following question arises: are the current detectors suitable to work directly in the fisheye domain? To answer this question, in this paper a benchmark dataset and objective evaluation measures are considered to evaluate the performances of the state-of-the-art detectors in the fisheye domain. Test images are properly generated starting from benchmark rectilinear images and considering different fisheye focal lengths. The experiments evaluate the performances of the detectors against both increasing fisheye distortion and the combination of the fisheye distortion with photometric and geometric variability of the image content. The experiments demonstrate that affine covariant detectors can be employed directly in the fisheye domain. Furthermore, although the transformation between the rectilinear and the fisheye coordinates is not affine, we show that the mapping can be locally approximated by linear functions with a small error.

One of the most common problems in the image forensics field is the reconstruction of the history of an image or a video. The data related to the characteristics of the camera that carried out the shooting, together with the reconstruction of the (possible) further processing, allow us to have some useful hints about the originality of the visual document under analysis. For example, if an image has been subjected to more than one JPEG compression, we can state that the considered image is not the exact bitstream generated by the camera at the time of shooting. It is then useful to estimate the quantization steps of the first compression, which, in case of JPEG images edited and then saved again in the same format, are no more available in the embedded metadata. In this paper, we present a novel algorithm to achieve this goal in case of double JPEG compressed images. The proposed approach copes with the case when the second quantization step is lower than the first one, exploiting the effects of successive quantizations followed by dequantizations. To improve the results of the estimation, a proper filtering strategy together with a function devoted to find the first quantization step, have been designed. Experimental results and comparisons with the state-of-the-art methods, confirm the effectiveness of the proposed approach.

Content-aware image resizing techniques allow to take into account the visual content of images during the resizing process. The basic idea beyond these algorithms is the removal of vertical and/or horizontal paths of pixels (i.e., seams) containing low salient information. In this paper, we present a method which exploits the gradient vector flow (GVF) of the image to establish the paths to be considered during the resizing. The relevance of each GVF path is straightforward derived from an energy map related to the magnitude of the GVF associated to the image to be resized. To make more relevant, the visual content of the images during the content-aware resizing, we also propose to select the generated GVF paths based on their visual saliency properties. In this way, visually important image regions are better preserved in the final resized image. The proposed technique has been tested, both qualitatively and quantitatively, by considering a representative data set of 1000 images labeled with corresponding salient objects (i.e., ground-truth maps). Experimental results demonstrate that our method preserves crucial salient regions better than other state-of-the-art algorithms.

To assess if a digital image has been (or not) doubly compressed is a challenging issue especially in forensics domain where could be fundamental clarify if, in addition to the compression at the time of shooting, the picture was decompressed (in some way) and then resaved. This is not a clear indication of forgery, but it guarantees that the image, probably, is not the original one. In this paper we propose a novel technique able to recover the coefficients of the first compression in a double compressed JPEG image under some assumptions. The proposed approach exploits how successive quantizations followed by dequantizations introduce some regularities (e.g., sequence of zero and not zero values) on the histograms of coefficient distributions that could be analyzed to recover the original compression parameters. Experimental results and comparisons with state of the art methods confirm the effectiveness of the proposed approach.

Content-aware image resizing is an effective technique that allows to take into account the visual content of images during the resizing process. The basic idea beyond these algorithms is the resizing of an image by considering vertical and/or horizontal paths of pixels (i.e., seams) which contain low salient information. In this paper we exploit the Gradient Vector Flow (GVF) of the image to establish the paths to be considered during the resizing. The relevance of each path is derived from a saliency map obtained by considering the magnitude of the GVF associated to the image under consideration. The proposed technique has been tested, both qualitatively and quantitatively, by considering a representative set of images labeled with corresponding salient objects (i.e., ground-truth maps). Experimental results demonstrate that our method preserves crucial salient regions better than other state-of-the-art algorithms.

The widespread use of classic and newest technologies available on Internet (e.g., emails, social networks, digital repositories) has induced a growing interest on systems able to protect the visual content against malicious manipulations that could be performed during their transmission. One of the main problems addressed in this context is the authentication of the image received in a communication. This task is usually performed by localizing the regions of the image which have been tampered. To this aim the aligned image should be first registered with the one at the sender by exploiting the information provided by a specific component of the forensic hash associated to the image. In this paper we propose a robust alignment method which makes use of an image hash component based on the Bag of Features paradigm. The proposed signature is attached to the image before transmission and then analyzed at destination to recover the geometric transformations which have been applied to the received image. The estimator is based on a voting procedure in the parameter space of the model used to recover the geometric transformation occurred into the manipulated image. The proposed image hash encodes the spatial distribution of the image features to deal with highly textured and contrasted tampering patterns. A block-wise tampering detection which exploits an histograms of oriented gradients representation is also proposed. A non-uniform quantization of the histogram of oriented gradient space is used to build the signature of each image block for tampering purposes. Experiments show that the proposed approach obtains good margin of performances with respect to state-of-the art methods.

Today many people in the world without any (or with little) knowledge about video recording, thanks to the widespread use of mobile devices (PDAs, mobile phones, etc.) take videos. However the unwanted movements of their hands typically blur and introduce disturbing jerkiness in the recorded sequences. A fundamental issue is the overall robustness with respect to different scene contents (indoor, outdoor, etc.) and conditions (illumination changes, moving objects, etc.). In this paper we propose an accurate and robust image alignment algorithm for video stabilization purposes based on a voting strategy. Experimental results confirm the effectiveness of the proposed approach.

The increasing use of low cost imaging devices and the innovations in terms of media distribution technologies induce a growing interest on technologies able to protect digital visual media against malicious manipulations of the visual contents. One of the main problems addressed in this research area is the blind detection of traces of forgery on an image obtained through the internet. Specifically, in this paper we consider the context of communications, where malicious image manipulations should be detected by a receiver. In the proposed method, an image hash based on the Bag of Visual Words paradigm is attached as signature to the image before trans mission. The forensic hash is then analyzed at destination to detect the geometric transformations which have been applied to the received image. This task is fundamental for further processing which usually assumes that the received image is aligned with the original one, as in the case of tampering detection systems. Experiments show that the proposed approach outperforms state-of-the art methods by obtaining a good margin in terms of performances.

The distribution of digital images with the classic and newest technologies available on Internet (e.g., emails, social networks, digital repositories) has induced a growing interest on systems able to protect the visual content against malicious manipulations that could be performed during their transmission. One of the main problems addressed in this context is the authentication of the image received in a communication. This task is usually performed by localizing the regions of the image which have been tampered. To this aim the received image should be first registered with the one at the sender by exploiting the information provided by a specific component of the forensic hash associated with the image. In this paper we propose a robust alignment method which makes use of an image signature based on the Bag of Features paradigm. The alignment is based on a voting procedure in the parameter space of the model used to recover the geometric transformation occurred into the manipulated image. Experiments show that the proposed approach obtains good margin in terms of performances with respect to state-of-the art methods.

Today, many people in the world without any (or with little) knowledge about video recording, thanks to the widespread use of mobile devices (personal digital assistants, mobile phones, etc.), take videos. However, the unwanted movements of their hands typically blur and introduce disturbing jerkiness in the recorded sequences. Many video stabilization techniques have been hence developed with different performances but only fast strategies can be implemented on embedded devices. A fundamental issue is the overall robustness with respect to different scene contents (indoor, outdoor, etc.) and conditions (illumination changes, moving objects, etc.). In this paper, we propose a fast and robust image alignment algorithm for video stabilization purposes. Our contribution is twofold: a fast and accurate block-based local motion estimator together with a robust alignment algorithm based on voting. Experimental results confirm the effectiveness of both local and global motion estimators.

Signal degradation impacts the final quality of images acquired using remote sensing radiometer. The effectiveness of a restoration algorithm strongly depends on two main factors: an accurate model of the disturbs introduced by the acquisition device and adaptation of the filtering method to image content. In this paper we target the first factor, by providing a solution for characterizing multispectral image signal degradation. A framework for estimating signal disturbs from heterogeneous sets of multispectral images is presented jointly with a voting-based technique for determining the best coefficients of the fitting equation. Tests conducted on multispectral images confirm the effectiveness of the proposed approach.

Digital 3D mosaics generation is a current trend of NPR (Non Photorealistic Rendering) field; in this demo we present an interactive system realized in JAVA where the user can simulate ancient mosaic in a 3D environment starting for any input image. Different simulation engines able to render the so-called "Opus Musivum"and "Opus Vermiculatum" are employed. Different parameters can be dynamically adjusted to obtain very impressive results.

Digital video stabilization enables to acquire video sequences without disturbing jerkiness by compensating unwanted camera movements. In this paper, we propose a novel fast image registration algorithm based on block matching. Unreliable motion vectors (i.e., not related with jitter movements) are properly filtered out by making use of ad-hoc rules taking into account local similarity, local “activity,” and matching effectiveness. Moreover, a temporal analysis of the relative error computed at each frame has been performed. Reliable information is then used to retrieve inter-frame transformation parameters. Experiments on real cases confirm the effectiveness of the proposed approach even in critical conditions.

Digital video stabilization allows to acquire video sequences without disturbing jerkiness, removing unwanted camera movements. In this paper we propose a novel fast video stabilization algorithm based on block matching of local motion vectors. Some of these vectors are properly filtered out by making use of ad-hoc rules taking into account local similarity, local ldquoactivityrdquo and matching effectiveness. Also a temporal analysis of the relative error computed at each frame has been achieved. Reliable information are then used to retrieve inter-frame transformation parameters. Experiments on real cases confirm the effectiveness of the proposed approach even in critical conditions.

In this paper we propose a novel fast fuzzy classifier able to find regular and low distorted near regular texture taking into account the constraints of video stabilization applications. Digital video stabilization allows to acquire video sequences without disturbing jerkiness, removing unwanted camera movements. In presence of regular or near regular texture, video stabilization approaches typically fail. These kind of patterns, due to their periodicity, create multiple matching that degrade motion estimation performances. The proposed classifier has been used as a filtering module in a block based video stabilization approach. Experiments on real sequences with (and without) regular texture confirm the effectiveness of the proposed approach.