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Malware researchers can convert a Windows Portable Executable (PE) into an RGB image without running it. They read the file as bytes or statically disassembled instructions, arrange those values on a two-dimensional grid, and assign three feature planes to red, green, and blue channels. The resulting PNG is not a photograph or a screenshot: it is a deterministic encoding of executable structure that a convolutional neural network (CNN) can process.
What is being converted into an image?
A Windows PE file is a structured binary containing headers, sections, machine code, data, resources and metadata. For image-based malware analysis, the file is treated as data. The analyst does not launch it, resolve its imports at runtime or observe its behavior. Instead, a program reads the file’s ordered values and converts them into numerical features.
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In the simplest byte-level method, every byte is an integer from 0 through 255. Those integers become pixel intensities. A value of 0 can be represented as black, 255 as white, and intermediate values as gray levels. The byte stream is then wrapped across rows of a chosen width. If the width is 256, for example, bytes 0–255 form the first row, bytes 256–511 form the second, and so on. The final row may need padding or filling when the file length is not an exact multiple of the width.
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Why use a picture instead of feeding bytes directly?
CNNs are designed to learn local patterns and combinations of patterns in grids. A binary file is one-dimensional, but placing its values on a grid allows image architectures to detect recurring neighborhoods, edges, textures and larger motifs. Transfer-learning models trained on image data can also be adapted to malware images, although the visual meaning of a malware image differs from the meaning of a natural photograph.
Visualization is mainly a feature-engineering and representation strategy. It can expose layout, repeated code or data regions, entropy changes and instruction patterns to a model that might otherwise require a specialized sequence architecture. It does not make the executable understandable to a human in the way a disassembler listing does, nor does it prove that the sample is malicious.
How the byte-to-pixel mapping works
Read the file as an ordered byte stream
The pipeline starts by opening the PE as binary data and reading its bytes in order. Researchers may use the complete file or restrict extraction to selected sections, such as executable code. The choice affects what the model sees: the full file includes headers, resources and possible overlays, while a code-section view emphasizes instructions.
Choose image geometry
An image needs a width and height, but PE files have variable lengths. A fixed width creates a variable height, so samples may later be padded or resized to the dimensions expected by a CNN. Some studies use an adaptive width based on file size; others use a predefined width and a filling rule. Filling can mean appending zeros, repeating a value or using another deterministic strategy. These choices are not cosmetic: they can create artifacts that a model learns as shortcuts.
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Convert values to intensities
Raw bytes already occupy the 0–255 range used by an 8-bit grayscale channel. Encoded instructions, entropy values or transition statistics may have a different range and require normalization. A reproducible implementation records the scaling rule, clipping behavior and whether normalization is calculated per file or across the training set.
Stack three planes into RGB
A grayscale byte image has one plane. An RGB image has three. Researchers can copy or transform one feature into each plane, or assign different information to each channel. The red, green and blue labels do not have an inherent malware meaning; they are positions in a three-channel tensor.
| Channel design | What a channel can contain | What it contributes |
|---|---|---|
| Direct byte mapping | Byte values arranged by file order | Raw binary structure and local byte neighborhoods |
| Byte plus metadata | Byte order, location information and entropy in separate planes | Combines content with where it occurs and how varied it is |
| Assembly-aware fusion | Raw bytes in one plane; opcode or instruction features in others | Combines low-level representation with code semantics |
| Transition-based encoding | Opcode transitions or Markov-transition statistics | Captures recurring instruction-to-instruction behavior |
| Duplicated or transformed planes | Related versions of the same feature | Matches an RGB input interface when three complementary views are available |
A channel can therefore represent spatially aligned data, such as byte value and local entropy at the same file offset, or a separately rasterized feature map. The key requirement is consistency: the same channel definition must be used for training, validation and deployment.
Two main ways researchers create malware RGB images
Raw-byte visualization
Raw-byte methods require no disassembly. The analyst maps the byte stream to one or more image planes, optionally adding location or entropy features. This approach is fast, works even when code is difficult to disassemble, and preserves the complete sequence selected for extraction. Its weakness is that the model must infer instruction boundaries and semantics from numeric patterns. Packers, encryption and large opaque data regions can make that difficult.
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Assembly and opcode visualization
Assembly-aware methods first disassemble the PE’s code section with a static-analysis tool. The extracted instruction sequence can be represented as opcode values, instruction metadata, opcode-frequency maps or opcode transitions. Markov-style transition matrices quantify how often one opcode follows another and can be rasterized as an image. The MCTVD work identifies IDA Pro as an example of a tool used to extract assembly instructions.
Assembly features can expose semantics that are not obvious in raw bytes, but disassembly is imperfect. Obfuscated control flow, packed sections, malformed files and data embedded in code can produce inaccurate instruction streams. A practical system should retain the extraction status and avoid silently treating failed disassembly as an ordinary sample.
A reproducible end-to-end workflow
- Collect and label samples. Gather benign and malicious PE files under a documented policy. Keep the binary label (benign or malicious) separate from malware-family and subfamily labels. Record collection dates, source identifiers and duplicate relationships.
- Define the extraction scope. Decide whether to process the complete file, selected PE sections or only the code section. Apply the same rule to every split unless the experiment explicitly compares scopes.
- Extract static content. Read bytes directly, or disassemble code with a static-analysis tool and derive opcodes, instruction metadata or transition statistics. Do not execute samples as part of image creation.
- Specify the mapping. Document row width, height policy, padding or filling, normalization, clipping and any resizing. A sample should produce the same tensor every time.
- Build the channels. Stack three feature planes into an RGB tensor. For example, one plane can contain byte order, another entropy and another a location-derived feature. An assembly-aware design can combine byte and opcode views.
- Export and verify. Save the tensor as a lossless image such as PNG when an image file is required. Check dimensions, channel order, value ranges and whether padding dominates short files.
- Split without leakage. Keep near-duplicate files, versions from the same campaign and related samples in one partition where possible. Randomly splitting exact or near duplicates can make performance look unrealistically high.
- Train the model. Use a custom CNN or a transfer-learning architecture. Tune preprocessing and augmentation only on the training data, then freeze the evaluation protocol.
- Evaluate the intended task. Report binary detection separately from family and subfamily attribution. Include per-class precision, recall and F1, along with a confusion matrix when class imbalance or many families are involved.
What the benchmark numbers actually show
Published results demonstrate that the representation can work well under particular experimental conditions, not that every RGB malware detector will achieve the same accuracy.
| Study or dataset | Reported result | Scope and qualification |
|---|---|---|
| Guo et al., Security and Communication Networks (2020) | 99.73% and 99.54% accuracy | Results reported on two malware datasets using combined byte, location and entropy visual features; they are not universal guarantees. |
| Saxena and Das, Scientific Reports (2026) | 99% binary accuracy; 96–97% family accuracy; 91–95% subfamily accuracy; macro F1 up to 0.89 | Attention-augmented transfer-learning models evaluated on RGB PE images for a 33-subfamily task. |
| Eroğlu Demirkan and Aydos, Applied Sciences (2025) | 98.45% top accuracy | Reported for an RGB assembly-visualization framework on MaleVisAsm experiments. |
| Microsoft Malware Classification Challenge BIG 2015 training set | 10,860 labeled samples across 9 malware families | Dataset size as described by the cited image-visualization studies; the result is a dataset property, not a model score. |
| Malimg dataset | 9,339 samples across 25 malware families | Dataset composition as described in the cited image-visualization study. |
Binary detection asks whether a file is malicious. Family classification asks which broad malware family it resembles. Subfamily attribution is finer-grained and usually harder. A model can score highly on the first task while performing poorly on the latter two, so a single accuracy number is not enough.
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Why image width, filling and normalization matter
Two systems can process the same executable and learn different decision rules because they rasterize it differently. A fixed width determines which bytes become neighbors. Padding can create a recognizable border or large constant region. Resizing changes the correspondence between a pixel and an original byte. Per-file normalization can erase absolute magnitude information, while global normalization preserves comparability across samples.
These decisions can also create accidental clues. If one family tends to contain larger files, the image height may reveal the label. If a collection source uses a distinctive wrapper or timestamp range, the CNN may learn that artifact rather than malware structure. Researchers should compare against simple baselines, inspect incorrectly classified files and test whether performance survives changes in geometry.
Can an RGB malware model generalize to new families?
Not automatically. A model trained on known families usually learns similarities represented in those families. A new family may use different packing, compiler output, APIs, section layouts or encryption. High random-split accuracy can therefore coexist with weak performance on future samples.
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Packing and obfuscation are especially important. They can change byte distributions and disrupt disassembly without changing the payload’s intent. Conversely, a model may learn packer signatures and classify the wrapper instead of the underlying malware. Robustness to adversarially modified binaries is a separate research question; the published literature does not establish a universal defense.
Operational limits and safe interpretation
- It is static analysis, not a behavior report. An image cannot by itself show network traffic, persistence, dropped files or runtime encryption.
- It is not a standardized format. There is no single universally accepted byte-to-RGB algorithm or best channel assignment.
- Disassembly can fail. Packed, malformed or deliberately confusing code may produce incomplete assembly features.
- Dataset bias can dominate. Duplicates, source artifacts and family imbalance can inflate apparent performance.
- Accuracy can hide minority-class failures. Use per-class metrics and confusion matrices, not accuracy alone.
- PNG is an encoding choice. Lossless storage avoids introducing compression artifacts, but the model still depends on the preprocessing that generated the pixels.
For a defensible deployment, preserve the original hash and extraction metadata, quarantine files during analysis, and treat the image classifier as one signal in a broader malware triage system. Static RGB classification complements sandboxing, signatures, structural PE features and analyst review; it does not replace them.
What to remember
The RGB image is a deterministic map of executable data. Researchers either rasterize bytes directly or derive assembly, entropy, location and transition features before stacking three planes into a CNN-ready tensor. Red, green and blue have no fixed malware semantics; their value comes from carrying complementary evidence. The image width, filling and normalization are part of the model, not neutral formatting details. Finally, impressive benchmark scores are tied to particular datasets, families, preprocessing rules and validation splits, so claims about generalization require tests on separated, time-aware or cross-dataset samples.
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